Figure AI22Nvidia13Skild AI12axis robotics9Google8Physical Intelligence7Unitree7Tesla6World Labs6Agibot5BMW5Hugging Face4video uploads4agentic video understanding3Agility Robotics3app downloads3SoftBank3a16z New Media2Applied Intuition2Apptronik2Booster Robotics2Dyna Robotics2Generalist2Hyundai2Lightwheel2napkin production2Pixomondo2Promise2Scale AI2The University of Hong Kong2total paid out2total payment2training data2Uber2UnitreeRobotics2Wayve2weekly active contributors2weekly active users23D inkjet printing13D reconstruction1A16z1ACE Robotics1AgiBot World platform comprises over 1 million trajectories.1Aigen1Aimalysheva1Amazon1Anthropic1Antioch1articulated assets1Atlas1Avala1average per-step success1average performance improvement of policies using the dataset.1badges for contributions in the AXIS campaign.1Bedrock1BlackRock1bmw-spartanburg-plant1Boston Dynamics1bottlenecks for scaling intelligent robots.1carbon reduction1Caterpillar1Caterpillar's operational data.1Churchill Capital XI1Columbia University1commitment on data and compute1commitment to data and compute for the next 12 months1contracted orders1crowns1Cruise1Current hours of available data for training robots.1Current throughput of valid trajectories collected per hour.1Dandy1data collection1Deep Robotics1Delivery timeline for a data center.1Demonstrations collected for the tasks.1deployments of Spot1Dexmal1Din Tai Fung1downloads1DROID dataset robot interaction data1Dyna1Dynamo Ventures1eastworlds_io1economic gains1Equinor1external funding round1factory-in-china1fal1Fetch Robotics1FieldAI1FigureAI1filtered simulation episodes1FORT Robotics1Foxglove1fremont1Geely Auto1GEN-1.5 model capabilities1General Catalyst1General Motors1Genesis AI1GGUF conversion and quantization1giga-texas1global market forecast for humanoid robots by 20271Gravis Robotics1Gritt1Groq1GrubMarket1GXO1H3 Max Turbo1Harvard University1historic crime scenes1Humanoid robots procurement1humanoid robots shipped in 20251humanoids built1Hydra-0 training1ICL scaling gains over language prompting are moderate for seen tasks.1Index uploads1Japan's postal service1JD.com1JINGDONG Industrials1lackawanna-energy-center1LG1Lightspeed1likes on Seohong Park's post1LimX Dynamics1Linker Vision1liuzhou-training-center1Lotus Cars1Madison Air1Manycore Tech1maximum number of times a user can complete the same task.1Mecka1Mercado Libre1Mercedes-Benz1Milk Road Pro1Mind Robotics1MiniMax1ministry-of-science-and-ict1MIT1Model merge process1Mostik1Munari1NBT average for RandER.1NBT average with 1% Memory Anchors removed.1NBT average with 10% Memory Anchors removed.1NBT average with 5% Memory Anchors removed.1Nemotron 3.5 Lightning on Jetson AGX Thor1Nexthop AI1Noble Machines1Number of contributors for the engine on Base.1Number of downloads of the Index platform.1Number of global contributors and collected trajectories on the platform.1number of objects in AgiBot World.1Number of tasks published.1OpenAI1payments made1payments made through Index1payments to data creators1payouts1pennsylvania-governor-josh-shapiro1performance comparison1performance improvement of the proposed policy over prior arts.1physical AI data delivered1PiPER1plant-spartanburg1Ports connecting NPUs within the rack.1Ports connecting to the network of switches.1proception1Projected growth in revenue from physical AI.1projected spending1Qatar Investment Authority1Qwen3.5-4B on Jetson Orin Nano1replies on Seohong Park's post1retweets on Seohong Park's post1Ricursive Intelligence1robot data collection1robot interaction data from the DROID dataset1robot learning economics1robot programming1robot trajectories1robot-park1Runway1S1 compared to current VLA models1S1 model capabilities1S1's performance on manipulation tasks1SambaNova1Samsung1Scale AI data delivery1Schaeffler1SDLC process rewriting1Seeed1Seeed Studio1Sequoia Capital1Sequoia's fundraising1Shanghai AI Lab1Shanghai Innovation Institute1Simulated robot bodies1south-korea-ministry-of-planning-and-budget1Spacex1spartanburg1Stanford University1status of physical AI companies1Stretch1Subscribers to SemiAnalysis.1sundayrobotics1Target valid hours of data per month from the Mobile Ego-Centric App.1teleoperation hours1the project stand not just as a piece of marketing, but as a vision statement: a world built by the very technology that made it possible.1Timeline for the plant potting task.1total memory demand with 1 million humanoid robots1total memory demand with 10 million humanoid robots1Total number of trajectories collected.1Total number of videos collected by Index.1total payments made1total production of humanoid robots1Total robotic systems procurement1Total trajectories in the engine.1Toyota Motor Manufacturing Canada1Toyota Research Institute1training hours for LDA-1B model1training needed for current VLA models to match S1's accuracy1transaction valuation1TRON 21UC Berkeley1UMI1UMI and teleoperation in robotics1Unitree上市估值1valuation1venture funding into physical AI1videos uploaded1weekly active users for data collection1Wonik Robotics1XPeng Robotics1XSquareRobot1Zhejiang University1ZiNovaLabs1Zite1国家发展改革委1开发者大会参与人数1特斯拉1
Who captures
Figure18Nvidia9axis robotics9Skild AI8@smsehy7World Labs4Google4Agibot3@stretchcloud3Physical Intelligence3Tesla2@SemiAnalysis_2The University of Hong Kong2Dyna Robotics2Unitree2Promise2@Biti88881OpenAI1Stretch1@binarybits1Munari1@rimtoln1Zite1Zhejiang University1XSquareRobot1@OperationsPLS1MiniMax1Mostik1Caterpillar1ZiNovaLabs1Anthropic1Noble Machines1eastworlds_io1@pstAsiatech1@NVIDIARobotics1UnitreeRobotics1Milk Road Pro1Sequoia Capital1Hyundai1@du_maximilian1https://x.com/poezhao06051@continuumlabs_1FigureAI1Agility Robotics1Dyna1Apptronik1Dynamo Ventures1@deepakpathak1https://x.com/huaijiangzhu1@gupta_abhinav_1https://x.com/adcock_brett1sundayrobotics1@EdmondIsARobot1https://x.com/seohong_park1https://x.com/RoboPapers1@RoboPapers1@TheHumanoidHub1@DrJimFan1Dandy1Mecka1Skild AI Team1https://www.facebook.com/485764111811BMW Group1Boston Dynamics1Stanford University1Kevin Black1Edmond1Hugging Face1
Sourced numbers
H3 Max Turbo$0.01“a $0.01/s video generation endpoint makes AI video economically viable for things like product demo generation, marketing content pipelines, and real-time synthetic data.”@stretchcloud on X
3D reconstruction25.3 units“Atlas claims mean AbsRel error of 25.3, better than specialist tools built for that task.”@stretchcloud on X
Hydra-0 training2202 hrs“It was trained on about 2,202 hours of multi-embodiment video spanning human hands, handheld grippers, single-arm robots and bimanual systems.”@techniahqrobot on X
data collection200 hrs“.@eastworlds_io is producing 200 hours of humanoid teleop data every week, making it the largest @UnitreeRobotics G1 data source outside China.”@0xconglomerate on X
Ports connecting NPUs within the rack.7 units“Seven ports are used to connect other NPUs on the same tray over flyover cables.”@SemiAnalysis_ on X
Ports connecting to the network of switches.4 units“This leaves the remaining 4 ports to connect to a network of low-radix-switches in the backplane for further intra-rack connectivity.”@SemiAnalysis_ on X
Nemotron 3.5 Lightning on Jetson AGX Thor44.8 hrs“On Jetson AGX Thor, the three training steps completed in 44.8 seconds and saved a 438 MB adapter.”Optimize Models with Unsloth | Jetson AI Lab
humanoid robots shipped in 20255,500 units“unitree shipped 5,500 humanoid robots in 2025 – more than tesla, figure ai and agility robotics combined.”@thehypedotnews on X
total production of humanoid robots6,500 units“total production exceeded 6,500 units.”@thehypedotnews on X
global market forecast for humanoid robots by 2027100,000 units“counterpoint forecasts the global market hitting 100,000 units by 2027 – 6x the 2025 numbers.”@thehypedotnews on X
total memory demand with 1 million humanoid robots$600million“that could represent approximately $600 million–$800 million in memory content, while 10 million robots could create a $6 billion–$8 billion opportunity.”@MelvinInvests on X
total memory demand with 10 million humanoid robots$6billion“while 10 million robots could create a $6 billion–$8 billion opportunity.”@MelvinInvests on X
carbon reduction1,780 units“Through smarter supply-demand matching and logistics optimisation, JINGDONG Industrials helps customers reduce unnecessary transportation and resource waste.”JD.com, Inc.
venture funding into physical AI$47.4B“Global venture funding into physical AI hit $47.4B across 521 deals in H1 2026 — nearly 4x the prior half.”@davidcao01 on X
Sequoia's fundraising$10B“Sequoia raised $10B four months after a $7B raise, explicitly reframing its thesis from "algorithms to atoms"”@davidcao01 on X
SoftBank's investment in Skild AI$1.4B“SoftBank led a $1.4B Series C into robotics firm Skild AI, tripling its valuation to $14B in seven months”@davidcao01 on X
teleoperation hours100000 hrs“Skild says the equivalent training data took to collect.”@Robot_AIsignals on X
average per-step success66 units“Skild reports 66% average per-step success on unseen tasks against 9% for a language-prompted baseline, once pre-training reaches 100,000 hours (company claim, internal evaluation).”@Robot_AIsignals on X
Index uploads16,000,000 units“Index has taken 16 million uploads from 108 countries,”@Robot_AIsignals on X
weekly active contributors44,000 units“carries 44,000+ weekly active contributors”@Robot_AIsignals on X
payments made through Index$15M“has paid out $15M — with over $1B committed to data and compute across the next twelve months (company claims).”@Robot_AIsignals on X
Number of global contributors and collected trajectories on the platform.150,000 units“As of late August 2026, the platform has surpassed 150,000 global contributors and collected over 3.7 million trajectories across 4,000+ published tasks.”Cicada Market Making on X: "https://t.co/26k9SK56mf" / X
Delivery timeline for a data center.30 hrs“Turner’s prefab arm xPL Offsite delivered a hot-aisle containment system for a 30MW rapid-deployment data center.”Dynamo Dispatch (2026/08/24) - by Santosh Sankar
weekly active users for data collection43,000 units“We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots”@adcock_brett on X
Simulated robot bodies100,000 units“They trained locomotion across 100,000 simulated robot bodies, which forces the policy to deal with different morphologies instead of just memorizing how one specific robot moves.”@0xconglomerate on X
robot data collection50 hrs“Practically takes much longer than 50-100hrs to collect 50hrs of robot data assuming people take break or make mistake too.”@deepakpathak on X
robot trajectories1 units“Every task you complete on Axis Hub is a robot trajectory — real training data for real robot policies.”@axisrobotics on X
app downloads264,000 units“264K app downloads across 108 countries”@TheHumanoidHub on X
weekly active contributors44,000 units“44K weekly active contributors”@TheHumanoidHub on X
videos uploaded16,000,000 units“16M videos uploaded, 30 minutes of video ingested every second”@TheHumanoidHub on X
commitment to data and compute for the next 12 months$1B“$1B committed to data and compute”@TheHumanoidHub on X
robot learning economics$1“you pay the enormous data bill once during foundation-model training, then amortize it across thousands of new tasks through prompting.”@rohanpaul_ai on X
S1's performance on manipulation tasks$380“one video demonstration provided roughly the benefit of 380 post-training examples!”@DeryaTR_ on X
@RoboPapers 209187717329235601230000 hrs“30,000 hours”@RoboPapers on X
articulated assets27 units“TaskGen now includes a library of 27 articulated object families (4 variants each).”@axisrobotics on X
robot programming1 units“If showing it once is enough, that changes both how fast a robot becomes useful and who can work with one.”@TheHumanoidHub on X
BMW Group production30,000 units“the Figure 02 robot supported the production of more than 30,000 BMW X3 vehicles over ten months.”Press-Information June 25th 2026
deployments of Spot1,500 units“With over 1,500 deployments, Spot is already teaching hundreds of companies how to work alongside autonomous mobile robots.”An Electric New Era for Atlas | Boston Dynamics
Training Data20000 hrs“N1.7 is pretrained on 20K hours of EgoScale human video data alongside diverse robot demonstrations.”NVIDIA/Isaac-GR00T
the project stand not just as a piece of marketing, but as a vision statement: a world built by the very technology that made it possible.1 units“The production fused artistry, engineering, and emotion into a single workflow, revealing what’s possible when imagination becomes interactive.”Bringing Marble to Life | World Labs
performance improvement of the proposed policy over prior arts.32 units“it is trained across diverse data corpus with a scalable performance of 32% gain compared to prior arts.”AgiBot World Colosseo: A Large-scale Manipulation Platform
The physical AI bottleneck is not token volume, but rich force-torque telemetry and tactile slip dynamics. Learning physical interaction requires real-world data pipelines that log contact physics at kilohertz sample rates. https://t.co/YoW0X7SyiX
11:15 PM ET
@smsehy
11:15 PM ET@smsehy
@smsehy on X
Augmenting sparse real-world physical demos with procedural simulation bridges early skill acquisition, but factory validation still demands testing across uncalibrated part tolerances and surface contamination. https://t.co/zD5ef4hsyk
11:14 PM ET
@smsehy
11:14 PM ET@smsehy
@smsehy on X
Training reinforcement learning on cached latent embeddings solves policy training throughput. The critical step for automotive manufacturing remains hard reliability optimization to eliminate rare edge-case failures during high-speed assembly. https://t.co/JZJfqC0HNe
11:11 PM ET
@smsehy
11:11 PM ET@smsehy
@smsehy on X
Crowdsourcing simulated trajectories accelerates high-level spatial priors. The engineering hurdle remains capturing high-frequency joint torque and tactile force vectors that cannot be generated without physical hardware interactions. https://t.co/PE1D6RrpiI
11:10 PM ET
@smsehy
11:10 PM ET@smsehy
@smsehy on X
255 million vehicles and global manufacturing plants already generate the physical sensor telemetry that foundation models need to learn real-world physics. Digital AI scales in datacenters, but physical AI requires industrial plants that understand ten-year hardware duty cycles. https://t.co/agvaM0yiMT
11:09 PM ET
@Biti8888
11:09 PM ET@Biti8888
@Biti8888 on X
The robot data gold rush is already happening in China. More than 90 robot gyms are launching, with 100+ robots in each center. Because the biggest bottleneck in humanoid robotics may no longer be building the robot. It’s teaching it how to work in the real world. Robots need enormous amounts of data, and an entire industry is now emerging to collect it. The gold rush isn’t just for robots. It’s for the data that will make them useful.
10:19 PM ET
AGAgibot
10:19 PM ETAGAgibot
@techniahqrobot on X
A big win for the robotics community AGIBOT just open sourced one of its most valuable real world datasets yet. @AGIBOTofficial has open-sourced AGIBOT WORLD 2026 Theme 3, a real-world dataset designed for reinforcement learning and embodied AI. The release contains 11,430 real-world robot trajectories across 14 industrial and household tasks. That includes • 1,024 successful policy rollouts • 1,369 failed policy rollouts • human in the loop corrections • external disturbances • task progress annotations • error-state annotations • human intervention data Most robot learning datasets focus…
10:14 PM ET
OPOpenAI
10:14 PM ETOPOpenAI
@DrJimFan on X
Good old days at OpenAI in 2016: an agent stares at screen pixels, moves a mouse, and books a flight on United. We called it World of Bits, inside OpenAI Universe. 10 yrs later, Astra is reincarnated in the same universe. Even the naming is astronomically correct 😆 Universe was perhaps the most ambitious AI infra project at the time, but we couldn't quite figure out how to solve it. A policy with zero prior knowledge of what a "submit" button does has to rediscover the entire internet visual lingua by trial and error. In retrospect, RL from scratch against hand-drawn, per-task "artisan"…
8:08 PM ET
STStretch
8:08 PM ETSTStretch
@stretchcloud on X
The harness problem is getting its own framework category. Tardigrade ships today: an agent harness built as typed state machine components over an immutable event log. The framing is React for harness. Each component knows its previous states. The whole harness is a pure function of the log. I keep seeing teams reach this conclusion from two directions. One: they try to build a stateful agent loop and hit the problem of debugging mid-run failures, resuming interrupted sessions, and auditing what the agent actually did and when. Two: they want to add AI behavior to existing Effect/functional…
2:58 PM ET
AGAgibot
2:58 PM ETAGAgibot
@spaceandtech_ on X
AGIBOT has officially open-sourced AGIBOT WORLD 2026 Theme 3: Reinforcement Learning, a real-world embodied AI dataset collected across expert demonstrations, autonomous policy rollouts, and human-in-the-loop corrections. It includes 11,430 real-world trajectories across 14 industrial and household tasks, capturing both successful and failed policy rollouts, along with detailed annotations for task progress, errors, disturbances, and human interventions. By learning from successes, failures, risks, and human corrections, robots can learn not only how to do it, but how to do it better.…
2:21 PM ET
@binarybits
2:21 PM ET@binarybits
@binarybits on X
This is a great illustration of why training robots manipulation tasks in simulation (middle clip) doesn't work very well. From Kai's excellent new piece on robot data. https://t.co/2ysIIvThKK https://t.co/VJDbshw5NR
2:07 PM ET
MUMunari
2:07 PM ETMUMunari
@frontrunvc on X
EARLY: @munariai - 6 followers (Robotics) Munari just launched. Operator scoring for robot training data. On @frontrunvc, the story started 47 days before the X account went live. jul 16: a tracked account follows @rmn. flagged. aug 11: flagged building. aug 19: website found. no x account yet. aug 30: a16z @speedrun SR007. sep 1: @munariai goes live. sep 4: 17 tracked accounts following the founders. a whole company, before it had a handle. this is timeline. coming soon to every company card on @frontrunvc.
1:43 PM ET
@rimtoln
1:43 PM ET@rimtoln
@rimtoln on X
EVERYONE SHOWS THE 300-AGENT SWARM. almost nobody shows what happens after 300 agents are easy to spawn turning 300 outputs into one structure is the hard part ▹ the pipeline nobody demos collect → connect → organize → unify one context graph at the end the swarm starts as hundreds of isolated research paths then sources start sharing entities links form · contradictions surface weak claims stay exposed eventually the mess collapses into one structure the whole system can reason over ▹ the reframe that's the part you usually never see the swarm is the demo the graph is the product bookmark…
1:32 PM ET
ZIZite
1:32 PM ETZIZite
@stretchcloud on X
The gap between "AI can help you build this" and "AI can actually deploy this for you" just got smaller. Zite released an MCP server that lets Claude, ChatGPT, and Cursor generate fully deployable business applications, not just code files you still have to wire up yourself. The model describes the app, Zite handles the infrastructure and deployment. This is a different category from code generation. Code generation gives you an artifact you own and maintain. Deployment-as-a-tool gives you a running application where the lifecycle management is abstracted away. The distinction matters for the…
1:19 PM ET
ZUZhejiang University
1:19 PM ETZUZhejiang University
@techniahqrobot on X
Zhejiang University just pushed long-horizon robot manipulation forward with HINT. HINT helps Vision-Language-Action models keep track of the human’s original intent across long tasks instead of losing the goal mid-sequence. On dual-arm PiPER robots, HINT raised π0.5 full-task success Fruit sorting 10% → 60% Word spelling 13.3% → 86.7% The system was also tested with unseen objects, layouts and instructions. Persistent intent tracking could become a key layer for reliable VLA systems and long-horizon Embodied AI.
12:36 PM ET
AGAgibot
12:36 PM ETAGAgibot
@Biti8888 on X
AGIBOT just open-sourced a new real-world dataset for embodied AI. @AGIBOTofficial AGIBOT WORLD 2026 Theme 3 focuses on something robots need just as much as successful demonstrations: failure and correction. The dataset includes 14 real-world tasks, 11,430 trajectories, detailed annotations of mistakes and interference, plus human-in-the-loop correction trajectories. Robots don’t improve by only seeing perfect executions. They need to learn what went wrong, how humans corrected it, and how to do better next time. https://t.co/41jAfUJ9HH
11:54 AM ET
Tesla
$TSLA
11:54 AM ETTesla
@Newsforce on X
The same activist network behind “No Kings” and other major protests is now mobilizing against data center construction and Flock license plate surveillance cameras, according to a Fox News Digital investigation and a new report. The report says organizations linked to the network have appeared across protests targeting Trump, Elon Musk, Tesla, Israel and ICE, with overlapping groups now turning their focus toward technology infrastructure and surveillance. It also highlights organizations funded by Neville Roy Singham, a Shanghai based tech billionaire whose nonprofit and media network has…
9:10 AM ET
Nvidia
$NVDA
9:10 AM ETNvidia
@CKCapitalxx on X
$NVDA just officially agreed to acquire Hugging Face for $12.93 billion. Hugging Face is where open source AI lives. Over 18 million developers, 3 million models, 500,000 datasets, and 200,000 companies using it to discover, test and deploy AI. Every open weight release lands there first. The detail most people will miss is that Nvidia was already the largest contributor to the platform. Over 500 models and 250 open datasets released there. They've been building inside this ecosystem for years, and now they own it. Here's what it opens up. >Distribution. Nvidia owns the compute. Hugging Face…
8:01 AM ET
XSXSquareRobot
8:01 AM ETXSXSquareRobot
@Biti8888 on X
The biggest bottleneck in dexterous robotics may not be the robot. It may be the data. @XSquareRobot’s TwinDEX is built around one idea: if you introduce errors during data collection, scaling the dataset won’t magically fix them later. That’s why TwinDEX matches the wearable collection device and robot hand across kinematics, contact surfaces, sensors and visual appearance. Human fingertips → wearable exoskeleton → robot fingertips. The result is high-fidelity robot-free data that can replace on-robot teleoperation for complex dexterous tasks. And with more than 5x higher collection…
5:00 AM ET
@smsehy
5:00 AM ET@smsehy
@smsehy on X
Managing private AI infrastructure requires treating data ingestion and model deployment like an industrial assembly line, where continuous telemetry monitoring and automated evaluation prevent silent production drift. https://t.co/jYGgdyTGUf
5:00 AM ET
@smsehy
5:00 AM ET@smsehy
@smsehy on X
Co-packaged optics and silicon photonics solve the physical thermal wall of high-speed copper interconnects, enabling low-latency data movement across massive distributed compute clusters without unsustainable electrical line losses. https://t.co/t5ZEAYO25J
4:13 AM ET
@OperationsPLS
4:13 AM ET@OperationsPLS
@OperationsPLS on X
Reshoring + robotics is a compounding equation. Labor cost parity via automation removes the offshore arbitrage. The real unlock is data sovereignty: domestic robot fleets generate operational data that compounds into moats. https://t.co/SPAMHZXiJF
3:47 AM ET
@stretchcloud
3:47 AM ET@stretchcloud
@stretchcloud on X
Background computer use is the feature that makes agents genuinely ambient. The agent runs while you work on something else. It clicks, types, opens apps. No babysitting. The next bottleneck is where agent clicks land: websites. Agents doing commerce, research, monitoring, or data work are spending a significant share of their compute just loading, re-reading, and re-extracting from pages they have already visited. The cost pattern I keep seeing: LLM extraction layers that call the model on every page visit. No memory of the selector. No self-healing when the site changes. The model reads the…
10:14 PM ET
@stretchcloud
10:14 PM ET@stretchcloud
@stretchcloud on X
Sodium v2 introduces a WebMCP layer. Any website can now serve structured data directly to an AI agent without HTML scraping. This is AEO (Agent Engine Optimization) in practice. The web is starting to build for agents, not just browsers. I built DeepScrape to handle exactly this: giving agents reliable, structured access to web data at inference time. https://t.co/JZYTWY8roq https://t.co/oUD6R0G9j7
8:14 PM ET
MIMiniMax
8:14 PM ETMIMiniMax
@stretchcloud on X
The pattern in AI inference is consistent: the fast-and-cheap version of a frontier model ships 2-4 weeks after the frontier version. H3 Max Turbo confirms this for video generation.H3 Max launched on August 26 at $0.08 per second of 768p output. H3 Max Turbo launches today at $0.01 per second, runs at 2x the speed, and hits the 97th percentile of H3 Max quality on evals. Seven days to a 50% cost reduction and 2x throughput improvement.The mechanism matters: fal built a separate inference engine specifically around MiniMax's H3 open-weights model architecture. They're not running H3 Max…
4:40 PM ET
@stretchcloud
4:40 PM ET@stretchcloud
@stretchcloud on X
Firecrawl's bet: the next billion users are agents. Their exact framing when they launched agent signups: "We're betting on the next 1B+ users being agents." Ask your agent to add Firecrawl, claim an API key, pull web data in seconds. The signal is correct. Agents need structured web data, not links and snippets. Web search gives pointers. What agents actually need is reliable extraction from specific pages at scale. The problem the whole category has not solved is cost. Re-reading a page with an LLM on every pass to extract data burns tokens fast. When a page changes, you re-read. When you…
4:33 PM ET
MOMostik
4:33 PM ETMOMostik
@Scobleizer on X
You have been paying a giant model to think and then to write. That second part is the expensive one. Mostik splits it. The giant model reads your problem. A small one on your own computer writes the answer. They share the thinking, not the words. 80% as good. 20 times faster. https://t.co/AD0GdYmAUg @mostik_ai @aimalysheva
3:00 PM ET
WLWorld Labs
3:00 PM ETWLWorld Labs
@stretchcloud on X
Something shifted with the World Labs Atlas announcement. We've had image generators, video generators, and 3D tools for years. Atlas does all three from a single model with the same spatial understanding. The technical description: a multimodal autoregressive diffusion transformer, pre-trained from scratch on image, video, 3D, and camera movement jointly. Not a video model with 3D features bolted on. One model, one spatial representation. What it can do: pixel-perfect camera control on generated frames up to 1440p. Video up to 60 seconds. Input a scene or image, move the camera in any…
9:43 AM ET
NVIDIA
$NVDA
9:43 AM ETNVIDIA
@techniahqrobot on X
We’re cooked if robots can start learning from bodies they don’t even have. NVIDIA Brown Columbia and Harvard researchers released Hydra-0, a world model that represents robot actions as pixel motion. It was trained on about 2,202 hours of multi-embodiment video spanning human hands, handheld grippers, single-arm robots and bimanual systems. Instead of tying an action to one specific robot body, Hydra-0 predicts how objects should move in the image. The team also tested it on a real 14-DoF YAM robot, which bent a flexible pipe from a desired object motion. This is still a controlled…
9:20 AM ET
CACaterpillar
9:20 AM ETCACaterpillar
Wall St Engine on X: "CATERPILLAR PARTNERS WITH FIELDAI ON PHYSICAL AI
$CAT is working with FieldAI to bring AI-powered autonomy and robotics into jobsites and factories, using Caterpillar’s operational data, FieldAI’s robot foundation models and NVIDIA technologies.
Early applications include au… / X
Post Log in Sign up Post Wall St Engine on X: "CATERPILLAR PARTNERS WITH FIELDAI ON PHYSICAL AI $CAT is working with FieldAI to bring AI-powered autonomy and robotics into jobsites and factories, using Caterpillar’s operational data, FieldAI’s robot foundation models and NVIDIA technologies. Early applications include autonomous inspections, real-time digital twins, risk detection and AI-driven optimization of equipment and facility operations." - Wall St Engine @wallstengine CATERPILLAR PARTNERS WITH FIELDAI ON PHYSICAL AI $CAT is working with FieldAI to bring AI-powered autonomy and…
11:48 PM ET
ARaxis robotics
11:48 PM ETARaxis robotics
Omai Leidi (3/3) on X: "Happy Wednesday frens👋
Everyone is watching the race to build better robots.
I’m starting to think the real race is the data teaching those robots how to act.
I’ve been digging into @axisrobotics, and it changed how I look at the space.
At first glance, AXIS looks like… / X
Post Log in Sign up Post Omai Leidi (3/3) on X: "Happy Wednesday frens👋 Everyone is watching the race to build better robots. I’m starting to think the real race is the data teaching those robots how to act. I’ve been digging into @axisrobotics, and it changed how I look at the space. At first glance, AXIS looks like a platform where you control simulated robots from your browser. That’s only the entry point. The bigger idea is robotic data infrastructure. Robots need experience to become useful. They need to pick things up, move objects, sort items, assemble parts, use tools, and operate in…
10:38 PM ET
Google
$GOOGL
10:38 PM ETGoogle
@OdinsDeposition on X
@dotkrueger They essentially pinpointed their long running targets today with Nvidia and Spacex CEOs both aligned on listing 10x economic gains from robotics. Prior to that 20-30% global economic gain from widespread digital AI.
10:31 PM ET
ZIZiNovaLabs
10:31 PM ETZIZiNovaLabs
LimX Dynamics on X: "Together with ZINOVA's Tool Intelligence, TRON 2 takes on increasingly complex construction workflows.
@ZiNovaLabs builds on TRON 2 to explore an innovative robotic configuration for construction, demonstrating key tasks in a scaled-down tilt-up construction workflow, includi… / X
Post Log in Sign up Post LimX Dynamics on X: "Together with ZINOVA's Tool Intelligence, TRON 2 takes on increasingly complex construction workflows. @ZiNovaLabs builds on TRON 2 to explore an innovative robotic configuration for construction, demonstrating key tasks in a scaled-down tilt-up construction workflow, including formwork assembly, multi-layer rebar placement and tying. TRON 2 serves as a modular and extensible embodied robotic platform for multi-tool, multi-step tasks across large workspaces. Its dual arms handle construction tools and materials across different orientations and…
2:21 PM ET
ANAnthropic
2:21 PM ETANAnthropic
Yuchen Jin on X: "Even if most AI labs are benchmark-maxxing now, Fable 5.1 still looks like an insane jump.
From Anthropic’s blog:
- Found a 1-in-a-million crash Millennium’s team couldn’t explain for 4–5 years
- 2x faster than Opus 5 while using half the tokens
Big if true." / X
Post Log in Sign up Post Yuchen Jin on X: "Even if most AI labs are benchmark-maxxing now, Fable 5.1 still looks like an insane jump. From Anthropic’s blog: \- Found a 1-in-a-million crash Millennium’s team couldn’t explain for 4–5 years \- 2x faster than Opus 5 while using half the tokens Big if true." - Yuchen Jin @Yuchenj\UW Even if most AI labs are benchmark-maxxing now, Fable 5.1 still looks like an insane jump. From Anthropic’s blog: \- Found a 1-in-a-million crash Millennium’s team couldn’t explain for 4–5 years \- 2x faster than Opus 5 while using half the tokens Big if true. View…
2:09 PM ET
NMNoble Machines
2:09 PM ETNMNoble Machines
From Robot Development to Deployment with Isaac GR00T & Jetson Thor - YouTube
Error 401 (Bad Request)!!1 401. That’s an error. The server cannot process the request because it is malformed. It should not be retried. That’s all we know. Back Skip navigation Search Search with your voice Sign in From Robot Development to Deployment with Isaac GR00T & Jetson Thor Tap to unmute 2x From Robot Development to Deployment with Isaac GR00T & Jetson Thor NVIDIA Omniverse 12,267 views Streamed 7d ago Copy link Info Shopping If playback doesn't begin shortly, try restarting your device. • You're signed out Videos you watch may be added to the TV's watch history and influence TV…
2:09 PM ET
Nvidia
$NVDA
2:09 PM ETNvidia
Seeed reBot Arm B601-RS: Physical AI & VLA Model Course | NVIDIA DLI Series
Warehouse China Warehouse US Warehouse Germany Warehouse The store will not work correctly in the case when cookies are disabled. US Warehouse: Enjoy FREE UNIUNI shipping on orders under $50! (Excludes XIAO & Raspberry Pi series) \\ \\ the AI Hardware Partner For Industry Products Local Warehouse Documents Customization Solution Software Support Open Claw Quick Order Account Get more with a SeeedStudio accountclear \\ \\ 1 on 1 Support \\ \\ Local Delivery \\ \\ 30-Day DOA Guarantee \\ \\ 30-Day Returns Sign In Create Account My Orders\\ Track, change, cancel Account Information\\ Update…
1:29 PM ET
Google
$GOOGL
1:29 PM ETGoogle
Google DeepMind on X: "We’re bringing agentic video understanding to our latest Gemini models.
They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵" / X
Post Log in Sign up Post Google DeepMind on X: "We’re bringing agentic video understanding to our latest Gemini models. They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵" - Google DeepMind @GoogleDeepMind We’re bringing agentic video understanding to our latest Gemini models. They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵 View media 1:29 PM · Sep 1, 2026127.2KViews 93 132 1.3K 264 - Google DeepMind @GoogleDeepMind 21h Instead of scanning an entire file, Gemini reasons across the video’s transcript, audio, and…
1:28 PM ET
WLworld-labs
1:28 PM ETWLworld-labs
Atlas: A World Model for Spatial Intelligence | World Labs
September 1, 2026Introducing Atlas, our new omni world model for spatial intelligence. Atlas: A World Model for Spatial Intelligence World models generate, reconstruct, and simulate any possible world. They understand how worlds appear, behave, and evolve so that we can render imagined worlds for creative users, simulate the real world in high fidelity, and help robots plan actions. At World Labs, we build these general purpose world models in pursuit of spatial intelligence. Today we are introducing Atlas, our next-generation world model. Atlas is an omni model that we pretrained from…
6:30 AM ET
EAeastworlds_io
6:30 AM ETEAeastworlds_io
@0xconglomerate on X
.@eastworlds_io is producing 200 hours of humanoid teleop data every week, making it the largest @UnitreeRobotics G1 data source outside China. That might look small beside known datasets, but they combine data collection and real-world robot deployment in one operation. This means they can use what happens in deployment to refine what they collect next. So instead of just adding more hours, the data can become more targeted, efficient, and cost-effective. The tradeoff is breadth. Eastworlds’ current Unitree G1 focus gives them depth on one embodiment, but not automatically the task and scene…
10:31 PM ET
Google
$GOOGL
10:31 PM ETGoogle
smartfitguide on X: "Google DeepMind's RT-2 model treats robot actions as text tokens. By co-fine-tuning pre-trained Vision-Language Models with robotic data, it achieved nearly double the performance on novel tasks compared to its predecessor, RT-1. #AI" / X
Post Log in Sign up Post smartfitguide on X: "Google DeepMind's RT-2 model treats robot actions as text tokens. By co-fine-tuning pre-trained Vision-Language Models with robotic data, it achieved nearly double the performance on novel tasks compared to its predecessor, RT-1. \#AI" - smartfitguide @bareani21645 Google DeepMind's RT-2 model treats robot actions as text tokens. By co-fine-tuning pre-trained Vision-Language Models with robotic data, it achieved nearly double the performance on novel tasks compared to its predecessor, RT-1. #AI View media 10:31 PM · Aug 31, 20268Views Reply 1
8:36 PM ET
Skild AI
8:36 PM ETSkild AI
The Robot Report: Skild AI unveils S1 robot foundation model | AI Understanding
Back to News ProductAI Understanding briefing The Robot Report: Skild AI unveils S1 robot foundation model The Robot Report reports that Skild AI unveiled S1, a robot foundation model that the company says can learn complex tasks from a single human demonstration video and operate across multiple robot forms. By AI Understanding EditorialSeptember 1, 2026 at 12:36 AM UTCUpdatedSeptember 1, 2026 at 1:47 AM UTC6 min read Read the primary source The short version The Robot Report reports that Skild AI unveiled S1, a robot foundation model that the company says can learn complex tasks from a…
8:00 PM ET
Google
$GOOGL
8:00 PM ETGoogle
Introducing Agentic Video in Gemini
Introducing agentic video understanding with Gemini Sep 01, 2026 \| 7 min read - x.com - Facebook - LinkedIn - Mail - Copy link Our new agentic feature for video analysis cuts token consumption by up to 88%, reduces costs by up to 66%, and boosts quality by up to 7%. Rohan Doshi Senior Product Manager, Google DeepMind Mario Lučić Research Director, Google DeepMind Share - x.com - Facebook - LinkedIn - Mail - Copy link Your browser does not support the audio element. Listen to article \[\[duration\]\] minutes This content is generated by Google AI. Generative AI is experimental…
5:45 PM ET
Nvidia
$NVDA
5:45 PM ETNvidia
@a16z on X
Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do…
5:21 PM ET
@pstAsiatech
5:21 PM ET@pstAsiatech
国家发改委：加速具身智能在制造、医疗等领域真实场景中落地应用_财经上下游_澎湃新闻-The Paper
Seven ports are used to connect other NPUs on the same tray over flyover cables. Seven ports connect across trays in the intra-rack 2D mesh over the backplane allowing each NPU0 to connect to all the other corresponding NPU0 within the rack. This leaves the remaining 4 ports to connect to a network of low-radix-switches in the backplane for further intra-rack connectivity. (2/4)
1:39 PM ET
@NVIDIARobotics
1:39 PM ET@NVIDIARobotics
Optimize Models with Unsloth | Jetson AI Lab
🎉 Welcome to Jetson AI Lab 2.0! We've redesigned the site with curated tutorials. Looking for old content? Browse the archive → × Back to Tutorials Optimize Models with Unsloth Fine-tune, convert and deploy language models on NVIDIA Jetson with Unsloth and llama.cpp. Author Aditya Sahu Fine-tune and deploy language models directly on NVIDIA Jetson with Unsloth and JetPack 7.2. This tutorial uses memory-efficient QLoRA, exports the results to GGUF, and runs them locally. Two practical examples cover Qwen3.5-4B on Jetson Orin Nano and NVIDIA Nemotron 3.5 Lightning 30B-A3B on Jetson AGX Thor.…
12:35 PM ET
UNUnitreeRobotics
12:35 PM ETUNUnitreeRobotics
@thehypedotnews on X
unitree robots now build themselves – and train while doing it @UnitreeRobotics deployed its embodied ai model inside its own factory in china. the robots aren't repeating pre-programmed movements – they perceive the environment, understand the task and perform real manufacturing operations on the production line. this isn't just production, it's also a data engine – every task the robots perform on the line generates real-world motion data that trains the next generation of models. the factory is both the product and the training ground. unitree shipped 5,500 humanoid robots in 2025 – more…
Skip to content You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert {{ message }} Uh oh! There was an error while loading. Please reload this page. hku-sail/ StreamPI Public - Notifications You must be signed in to change notification settings - Fork\\ 4 - Star\\ 161 main 1 Branch 0 Tags Go to Branches pageGo to Tags page Go to file Code Open more actions menu Latest commit happinesslz add multi-node training…
8:57 AM ET
MRMilk Road Pro
8:57 AM ETMRMilk Road Pro
@MelvinInvests on X
The next memory supercycle is hiding inside humanoid robots (Save this). This chart estimates that each humanoid robot could contain $600–$800 or more in memory related and semiconductor components, including DRAM, HBM, NAND flash, sensors, communication chips, and other devices. If manufacturers eventually produce 1 million humanoid robots, that could represent approximately $600 million–$800 million in memory content, while 10 million robots could create a $6 billion–$8 billion opportunity. However, the larger opportunity may come from the data centers that train these robots and process…
5:11 AM ET
Figure
5:11 AM ETFigure
JD.com, Inc.
Authentic Products Delivered Today \\ AI-Powered, ESG-Embedded Industrial Supply Chain Innovation Earns JINGDONG Industrials 2026 Sedex Technoical Innovation Award \\ \\ BEIJING, 27 August 2026 — JINGDONG Industrials, a leading industrial supply chain technology and service provider under JD.com, has won the Technical Innovation Award among the 2026 Sedex Supply Chain Awards, recognising its use of AI and technical solutions to embed ESG capabilities into industrial supply chain operations. The company was also nominated for the Social Impact Award. JINGDONG Industrials was the only…
5:05 AM ET
ARAxis Robotics
5:05 AM ETARAxis Robotics
@GabrielnSpace on X
i thought community robot data would hit diminishing returns fast but Axis Dataset V1 just made that take look stupid @axisrobotics pushed π0.5 from 83.9% to 88.8% on LIBERO Plus, and performance kept climbing as training data scaled from 25% to 100% with no obvious saturation 3m trajectories later the crowd is starting to look less like users and more like a distributed robotics lab #PhysicalAI
4:00 AM ET
Figure
4:00 AM ETFigure
How Figure Became the Biggest Name in Robotics | XMAQUINA DAO
Genesis Auction Wave 2! Launches June 24 Get DEUS homeDAO Portal How Figure Became the Biggest Name in Robotics Color theme: Four years, three generations of humanoids, a $39 billion valuation and now one of the largest physical AI data engines ever built. .png) August 27, 2026 Category: Physical AI Read time: 9 minutes Share This: Four years ago, Figure didn’t have a robot. Today, the company is valued at $39 billion, has built more than 1,000 humanoids, has robots working inside BMW, is preparing deployments with another major US retailer, and has developed its own AI models, manufacturing…
3:51 AM ET
Physical Intelligence
3:51 AM ETPhysical Intelligence
[2604.15483] $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Skip to main content Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2604.15483 (cs) \Submitted on 16 Apr 2026 ( [v1), last revised 24 Apr 2026 (this version, v2)\] Title:π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities Authors: Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, Vedant Choudhary, Foster Collins, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Maitrayee Dhaka, Jared DiCarlo, Danny…
7:11 PM ET
SCSequoia Capital
7:11 PM ETSCSequoia Capital
@davidcao01 on X
Fifteen years after "software is eating the world," Marc Andreessen's own firm just admitted atoms are eating software's margins. SUMMARY This is not an a16z story. a16z's $1.1B Machine Age Fund is simply the loudest confirmation yet of a rotation already underway across Silicon Valley's largest pools of capital — Sequoia Capital , Lightspeed , General Catalyst , and SoftBank have all made comparable moves in 2026. Global venture funding into physical AI hit $47.4B across 521 deals in H1 2026 — nearly 4x the prior half. For builders and investors in robotics, autonomous systems, and…
7:11 PM ET
Skild AI
7:11 PM ETSkild AI
@Robot_AIsignals on X
Show it once. That is the entire instruction. In a post published in August, Skild AI introduced S1, a robotics foundation model prompted with a video demonstration in place of a written command. The company says the same frozen weights then performed four tasks absent from its pre-training — potting a plant, cooking pancakes, brewing pour-over coffee, assembling a kit. Skild builds Skild Brain, which it calls the first unified robotics foundation model to generalise across both tasks and robot hardware. It raised $1.4bn in January at a valuation the company puts above $14 billion (company…
4:06 PM ET
Figure
4:06 PM ETFigure
@Robot_AIsignals on X
The robot industry's data shortage just became a gig job. On 25 August Figure launched Index, an app that pays people by the minute to film themselves doing ordinary household and workplace tasks. That footage trains Helix, the AI stack running its humanoid. Figure builds the Figure 03 humanoid and the Helix system inside it, and says its Series C exceeded $1B at a $39 billion post-money valuation (company claim). Figure says Index has taken 16 million uploads from 108 countries, ingests roughly 30 minutes of footage every second, carries 44,000+ weekly active contributors and has paid out…
11:00 AM ET
Nvidia
$NVDA
11:00 AM ETNvidia
Nvidia Wants to Run the World’s Robots. China Is an Eager Customer. - WSJ
Skip to Main Content Skip to... Select - What to Read Next - Most Popular News - Most Popular Opinion DJIA\\ \\ 52766.88\\ \\ -0.79% S&P 500\\ \\ 7631.47\\ \\ -0.71% Nasdaq\\ \\ 26099.77\\ \\ -1.03% Russell 2000\\ \\ 2920.13\\ \\ -1.23% U.S. 10 Yr\\ \\ -1/32\\ \\ 4.804% VIX\\ \\ 16.34\\ \\ 9.52% Gold\\ \\ 4378.40\\ \\ -0.41% Bitcoin\\ \\ 77402.94\\ \\ 0.18% Crude Oil\\ \\ 90.77\\ \\ 0.61% Dollar Index\\ \\ 95.91\\ \\ 0.01% KBW Nasdaq Bank Index\\ \\ 183.86\\ \\ -0.84% S&P GSCI Index Spot\\ \\ 735.66\\ \\ 0.17% Advertisement This copy is for your personal, non-commercial use only. Distribution…
6:01 AM ET
HYHyundai
6:01 AM ETHYHyundai
South Korea Commits KRW 2.3 Trillion to Build Full-Stack Humanoid Robotics Ecosystem by 2030 | Humanoids Daily
Search articles Key Takeaways Show all - Dedicated Humanoid Capital: South Korea is investing KRW 2.3 trillion ($1.66 billion) through 2030 to build an indigenous, full-stack humanoid robotics industry, backed by an additional KRW 2.8 trillion ($2.0 billion) for field verification. - Aggressive Localization Targets: The plan seeks to raise domestic localization of core humanoid components from 45% to 80%, transitioning legacy auto parts manufacturers into precision robotics suppliers. - State Procurement Pipeline: The government will purchase 1,080 humanoid robots and roughly 5,000 total…
4:46 PM ET
@SemiAnalysis_
4:46 PM ET@SemiAnalysis_
SemiAnalysis ChipBook
Manage Consent To provide the best experience, we use cookies to store and/or access device information. FunctionalFunctional Always active The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network. PreferencesPreferences The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.…
1:30 PM ET
@du_maximilian
1:30 PM ET@du_maximilian
MemoryAnchors
01 · Continual Learning Challenge Data Sensitivity in Continual Robot Learning To understand Memory Anchors, we first need to take a closer look at why robot policies forget old tasks when learning new ones. Sequential Task Learning We explore the problem setting of imitation learning on tasks in sequence. Drag the slider to step through ten tasks. The horizontal axis is the learning progress, and the vertical axis is the evaluated tasks. The lighter the off-diagonal elements, the worse the forgetting. ER buffer size 0.5% (Tiny)1% (Small)5% (Medium) Training stage 12345678910 Stage 5 of 10…
11:19 AM ET
ARAxis Robotics
11:19 AM ETARAxis Robotics
Cicada Market Making on X: "https://t.co/26k9SK56mf" / X
Post Log in Sign up Post - Cicada Market Making @cicada\mm Inside Robotics and Physical AI Featuring insights from Axis Robotics In 2026, the artificial intelligence industry learned to solve the compute problem. GPUs are becoming more accessible, models are cheaper to run inference on, cloud infrastructure keeps growing. But the next wave of AI, robotics and Physical AI, has a completely different problem. It all comes down to data that simply does not exist in the volume needed. No Internet for Robots LLMs grew out of a foundation that already existed. Decades of text on the internet,…
6:51 AM ET
https://x.com/poezhao0605
6:51 AM EThttps://x.com/poezhao0605
Poe Zhao on X: "Reuters just published a special investigation on China's humanoid robot boom. I contributed analysis at three points, and together they form one argument.
Start with pricing. China's capital markets have priced humanoid technology very aggressively. Investors are essentially betti… / X
Post Log in Sign up Post Poe Zhao on X: "Reuters just published a special investigation on China's humanoid robot boom. I contributed analysis at three points, and together they form one argument. Start with pricing. China's capital markets have priced humanoid technology very aggressively. Investors are essentially betting on future general-purpose usefulness, not current capability. Then look at where the robots actually work. The economics do not support widespread deployment today. Near-term demand will stay concentrated in dangerous, repetitive and structured work. The real long-term…
5:35 AM ET
@continuumlabs_
5:35 AM ET@continuumlabs_
@continuumlabs_ on X
Physical AI has made huge progress, but scaling intelligent robots is still constrained by problems far beyond the model itself. 7 bottlenecks stand out: 1️⃣ Data: robots need massive amounts of diverse real-world data, and collecting it is slow and expensive. 2️⃣ Hardware: capable robots are still difficult to manufacture, maintain, and deploy at scale. 3️⃣ Dexterity: manipulating objects precisely remains much harder than navigating or recognizing them. 4️⃣ Reliability: real environments are full of edge cases that simulations and controlled tests don't capture. 5️⃣ Cost: expensive…
4:00 AM ET
FIFigureAI
4:00 AM ETFIFigureAI
XMAQUINA on X: "This week @FigureAI launches Index, the largest physical robot training dataset in the world.
30 minutes of video uploads per second.
16M video uploads.
$15M paid to data creators.
Figure says it's committing $1B over the next 12 months on data and compute." / X
Post Log in Sign up Post XMAQUINA on X: "This week @FigureAI launches Index, the largest physical robot training dataset in the world. 30 minutes of video uploads per second. 16M video uploads. $15M paid to data creators. Figure says it's committing $1B over the next 12 months on data and compute." - XMAQUINA @xmaquina This week @FigureAI launches Index, the largest physical robot training dataset in the world. 30 minutes of video uploads per second. 16M video uploads. $15M paid to data creators. Figure says it's committing $1B over the next 12 months on data and compute. 00:00 View media…
2:00 AM ET
ARAxis Robotics
2:00 AM ETARAxis Robotics
@axisrobotics on X
Announcing our partnership with @Dexmal_AI About Dexmal: Dexmal is a technology company focused on general-purpose embodied intelligence, committed to building intelligent, useful, and trustworthy robots. As a core data infrastructure partner, Axis is teaming up with Dexmal to empower their VLA and world models through large-scale egocentric, simulation, and real-world data production. Backed by successful enterprise data deployments, this milestone highlights the scale, precision, and production-grade reliability of Axis’s compounding physical data engine. Together, we are establishing the…
1:43 AM ET
ARAxis Robotics
1:43 AM ETARAxis Robotics
@em3kagmi on X
Many People believe Physical might be unachievable not because robots lack arms or GPUs rather, because robots do not have enough real training data. Normal AI LLM can train on text that already exists on the internet but Robots cannot. A robot needs recordings of physical actions like how an arm moves, how a grasp starts to slip, or how an object sits at a slightly different angle. That data is expensive to collect in a lab, slow to produce, and usually too clean to match the real world. @axisrobotics approaches this differently by using a distributed network to collect physical interaction…
7:05 PM ET
Tesla
$TSLA
7:05 PM ETTesla
Optimus Just Entered Production at Fremont. Here's What Changes for Tesla Investors. | The Motley Fool
$CCXI 🤖💰 CCXI / Agility Robotics Study #10✔️ The Big Question — What Could AGLT Actually Be Worth?✔️ The conclusion first: Agility’s $2.5B transaction valuation is beginning to look increasingly conservative. But we should not simply assume that CCXI at $10 = Agility at $2.5B. Once the expected post-merger share count is considered, the market-implied valuation becomes considerably higher. And this is where $15, $20, and $25 start to mean very different things. ① The Starting Point — $2.5B The Agility Robotics / Churchill Capital XI transaction was announced with: • $2.5B pre-money equity…
3:20 PM ET
DYDyna
3:20 PM ETDYDyna
@Rewkang on X
Most Physical AI companies are still doing lab demos. A key factor in our investment in Dyna last year was their world class post training expertise/results and their deployment focus. More deployments begets better data begets better models begets faster time to deployment We saw this loop play out for multimodal models like ChatGPT/Claude and autonomous driving and we're seeing it play out for robotics
2:13 PM ET
DRDyna Robotics
2:13 PM ETDRDyna Robotics
Dyna Robotics on X: "After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now.
Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaur… / X
Post Log in Sign up Post Dyna Robotics on X: "After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting…
2:13 PM ET
DRDyna Robotics
2:13 PM ETDRDyna Robotics
Not Just a Model, But a Product — DYNA
Contact11:33 AM Name Email Company Team Size 1-2021-5051-100100+ Message BusinessMediaInvestorOther Submit Sections: 01Not just a model, but a product02"Both" is our way03How Dyna-2 closed the gaps04Deployment is the eval05The deployment flywheel \[ Research \] Not Just a Model, But a Product Category: Research Author: Dyna Robotics Date: August 2026 Read: 17 min \[Sound\]\[ Fullscreen \] 1.Not just a model, but a product Today, we're really excited to share an announcement our team has been working toward over the past year: our robots have successfully crossed the ROI threshold, and Din Tai…
4:00 AM ET
ARAxis Robotics
4:00 AM ETARAxis Robotics
Axis Robotics × Booster: From Digital Twins to a Robot Data Engine
Back How task-aligned simulation, real demonstrations, and continued pretraining can make each new Booster task easier to build than the last. Robot learning has a model problem. More importantly, it has a data-operations problem. Figure — Axis’s simulation solution spans third-person, head, and wrist observations while varying appearance, materials, object layout, lighting, and scene context. The objective is to preserve task structure while varying the details that should not control the policy. Introduction For robotics companies building new embodiments—and, increasingly, for the entire…
Rhys on X: "Cool to see a bunch of new robotics foundation model releases:
π0.7 from @physical_int
GEN-1.5 from @generalistAI
S1 from @SkildAI
Feels like the GPT-3 moment for robotics. Robot Models are Few-Shot Learners!" / X
Post Log in Sign up Post Rhys on X: "Cool to see a bunch of new robotics foundation model releases: π0.7 from @physical\int GEN-1.5 from @generalistAI S1 from @SkildAI Feels like the GPT-3 moment for robotics. Robot Models are Few-Shot Learners!" - Rhys @RhysLindmark Cool to see a bunch of new robotics foundation model releases: π0.7 from @physical\int GEN-1.5 from @GeneralistAI S1 from @SkildAI Feels like the GPT-3 moment for robotics. Robot Models are Few-Shot Learners! View media View media 2:45 PM · Aug 26, 2026255Views 3 3 - Rhys @RhysLindmark Aug 26 Also damn, still so early for robot…
1:24 PM ET
Apptronik
1:24 PM ETApptronik
Humanoids are for marketing, robots are the real business
READ00% The Apollo that Apptronik showed the world at the end of June has two legs, a torso, a head and the general proportions of a five-foot-eight adult. The Apollo its paying customers keep asking for has a wheeled base instead. Apptronik, an Austin robotics company that has raised nearly a billion dollars to build a humanoid, now ships its flagship in two configurations, and the one destined for warehouses rolls. Logistics buyers, it turns out, want stability, battery life and predictability more than they want a machine that walks. The case for legs is a good one, and the company has…
12:08 PM ET
DVDynamo Ventures
12:08 PM ETDVDynamo Ventures
Dynamo Dispatch (2026/08/24) - by Santosh Sankar
Dispatch by Dynamo Ventures SubscribeSign in Discover more from Dispatch by Dynamo Ventures The signal in the noise of the industrial renaissance. Each week, we distill the headlines that matter across the physical economy along with our unique perspective and opinion. Join the thousands of founders, executives, and investors who read it. Over 4,000 subscribers Subscribe By subscribing, you agree Substack's Terms of Use, and acknowledge its Information Collection Notice and Privacy Policy. Already have an account? Sign in Dynamo Dispatch (2026/08/24) Issue 384 \| The grid, the canal, and the…
10:59 AM ET
Figure
10:59 AM ETFigure
Brett Adcock on X: "We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots
This data collection project, Index, is our answer to the data problem: the largest useful robot training dataset in the world
Extrapolate out, and this is the pretraining ne… / X
Post Log in Sign up Post Brett Adcock on X: "We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots This data collection project, Index, is our answer to the data problem: the largest useful robot training dataset in the world Extrapolate out, and this is the pretraining needed for large scale robot generalization The effort started by purchasing data from vendors but this data was scarce and really poor quality - there was simply no way to make this work correctly at scale. So we did it ourselves. It was quite a massive effort that has now…
10:59 AM ET
Figure
10:59 AM ETFigure
@adcock_brett on X
We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots This data collection project, Index, is our answer to the data problem: the largest useful robot training dataset in the world Extrapolate out, and this is the pretraining needed for large scale robot generalization The effort started by purchasing data from vendors but this data was scarce and really poor quality - there was simply no way to make this work correctly at scale. So we did it ourselves. It was quite a massive effort that has now resulted in a Figure-owned data pipeline that is…
10:02 AM ET
Skild AI
10:02 AM ETSkild AI
@ThomasSmale on X
$14B valuation. Pre-revenue. A handful of customers. That is Skild AI today. And it is not the outlier. Physical Intelligence: $11B Generalist: $2B to $3B in months, $600M raised Skild AI: $14B Three companies. Billions in capital. Almost no commercial traction between them. The thesis is simple. Someone builds the foundation model for robotics the way OpenAI built one for language. Find the winner early, own the platform forever. Here is the problem. LLMs were trained on trillions of words scraped from the open internet. That dataset already existed, and it was free. No equivalent exists for…
9:30 AM ET
Unitree
9:30 AM ETUnitree
Robot brain builders are pushing out of their GPT-2 era | TechCrunch
Checking your Browser… Verifying... Stuck? Troubleshoot Success! Verification failed Troubleshoot Verification expired Refresh Verification expired Refresh Troubleshoot Cloudflare, opens in a new tab Privacy • Help Skip to content Image Credits: Kiyoshi Ota/Bloomberg / Getty Images Robotics Share on FacebookShare on XShare on LinkedInShare on RedditShare over EmailCopy Share Link Robot brain builders are pushing out of their GPT-2 era Tim Fernholz 6:30 AM PDT · August 26, 2026 Share on FacebookShare on XShare on LinkedInShare on RedditShare over EmailCopy Share Link Physical AI is one of the…
6:09 AM ET
Skild
6:09 AM ETSkild
@0xconglomerate on X
Is it really possible for a robot to learn a new task from one human video, with zero fine-tuning? I’ve read a lot of hype around Skild’s S1, with some already calling it robotics’ “ChatGPT moment.” That sounded a bit absurd to me, so I looked into what’s actually going on 👇 ➦ What is @SkildAI actually claiming? I’ve written enough marketing material for AI, robotics, and tech companies that I’m usually skeptical when something sounds too good to be true. If you only saw the announcement post, you could easily think the robot just watches one human video and somehow learns the task from that…
2:33 AM ET
@deepakpathak
2:33 AM ET@deepakpathak
@deepakpathak on X
@JieWang_ZJUI These are 5-10min long tasks, ~500 demos needed for VLAs. Practically takes much longer than 50-100hrs to collect 50hrs of robot data assuming people take break or make mistake too.
2:18 AM ET
ARAxis Robotics
2:18 AM ETARAxis Robotics
@axisrobotics on X
2/ What you're actually doing Axis is the compounding data engine for physical AI. Every task you complete on Axis Hub is a robot trajectory — real training data for real robot policies. You do the demonstration, we verify it, you sign it on-chain. Your contribution becomes a permanent record. Learn more:
7:06 PM ET
Figure
7:06 PM ETFigure
@TheHumanoidHub on X
Figure takes vertical integration to another level. Figure built its own egocentric data collection pipeline. Index is a crowdsourcing platform where anyone can collect data. - 264K app downloads across 108 countries - 44K weekly active contributors - 16M videos uploaded, 30 minutes of video ingested every second - $15M already paid out to contributors Figure says data vendors couldn't hit the throughput, diversity, or quality bar, so they built the pipeline themselves. Next 12 months: $1B committed to data and compute.
5:27 PM ET
https://x.com/huaijiangzhu
5:27 PM EThttps://x.com/huaijiangzhu
huaijiang on X: "ok, so if i understood this correctly, the “in-context learning” ability was essentially baked into the model at training time. roughly speaking, the policy is trained to do something like:
(demo video, current obs) → action
this is quite different from in-context learning in llm… / X
Post Log in Sign up Post huaijiang on X: "ok, so if i understood this correctly, the “in-context learning” ability was essentially baked into the model at training time. roughly speaking, the policy is trained to do something like: (demo video, current obs) → action this is quite different from in-context learning in llms, where the model is simply trained on next-token prediction over arbitrary context. fwiw, this is pretty clever because it could also solve another problem: they could pair an egocentric video-only demonstration with a UMI/teleop trajectory for the same task. the target…
2:05 PM ET
Skild
2:05 PM ETSkild
@rohanpaul_ai on X
Robotics is more and more getting close to get its version of in-context learning. Skild just released S1, a robot foundation model that uses video demonstrations to define tasks instead of language instructions. Give S1 1 human video showing a long, multi-step task, and the robot executes it straight away. No retraining. No fine-tuning. If this scales, you pay the enormous data bill once during foundation-model training, then amortize it across thousands of new tasks through prompting. That could change the economics of robot learning completely.
2:04 PM ET
Skild
2:04 PM ETSkild
@DeryaTR_ on X
This is super exciting advance in robotics AI! I believe S1 is the most impressive robot foundation model I had seen! S1, is a robot foundation model built to learn new manipulation tasks through in-context prompting In Skild’s internal benchmarks, S1 achieved a 66% step-success rate on unseen tasks versus 9% for a language-prompted model, while one video demonstration provided roughly the benefit of 380 post-training examples!
1:54 PM ET
@gupta_abhinav_
1:54 PM ET@gupta_abhinav_
@gupta_abhinav_ on X
@0fir0z @deepakpathak In the silicon valley world the race for robotics companies is to be similar to LLMs. But actually robotics needs many more concepts from computer vision. We are proud to say S1 is using so many of our past efforts in computer vision CC: @JitendraMalikCV
1:38 PM ET
Figure
1:38 PM ETFigure
Figure on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute
Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" / X
Post Log in Sign up Post Figure on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure…
1:36 PM ET
Figure
1:36 PM ETFigure
Figure on X: "By the numbers:
→ While in stealth, we crossed 264,000 app downloads
→ Uploading over 30 min of video very second
→ Over 16M video uploads
→ Paid out $15M to date
Weekly active users is now over 43,000" / X
Post Log in Sign up Post Figure on X: "By the numbers: → While in stealth, we crossed 264,000 app downloads → Uploading over 30 min of video very second → Over 16M video uploads → Paid out $15M to date Weekly active users is now over 43,000" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure @Figure\robot Aug 25 The data needed to scale a truly general purpose robots…
1:35 PM ET
Figure
1:35 PM ETFigure
Figure on X: "We've now collected data in 108 countries to date
Here's a global map of our collection, now updating live on our website, each point of light represents a real world contributions to our dataset" / X
Post Log in Sign up Post Figure on X: "We've now collected data in 108 countries to date Here's a global map of our collection, now updating live on our website, each point of light represents a real world contributions to our dataset" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure @Figure\robot Aug 25 The data needed to scale a truly general purpose robots…
1:35 PM ET
https://x.com/adcock_brett
1:35 PM EThttps://x.com/adcock_brett
Brett Adcock on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute
Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" / X
Post Log in Sign up Post Brett Adcock on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" - Brett Adcock @adcock\brett Aug 25 Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next…
1:34 PM ET
Figure
1:34 PM ETFigure
Figure on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world
For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" / X
Post Log in Sign up Post Figure on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure…
1:33 PM ET
Figure
1:33 PM ETFigure
Brett Adcock on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world
For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" / X
Post Log in Sign up Post Brett Adcock on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" - Brett Adcock @adcock\brett Aug 25 Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Brett Adcock on X: "Introducing Index
Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world
→ 30min of video uploads/sec
→ 16M video uploads
→ Paid $15M to date
→ 264k downloads
We're committed to spending $1B the next 12 months on data & com… / X
Post Log in Sign up Post Brett Adcock on X: "Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute" - Brett Adcock @adcock\brett Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Brett Adcock on X: "Introducing Index
Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world
→ 30min of video uploads/sec
→ 16M video uploads
→ Paid $15M to date
→ 264k downloads
We're committed to spending $1B the next 12 months on data & com… / X
Post Log in Sign up Post Brett Adcock on X: "Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute" - Brett Adcock @adcock\brett Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Brett Adcock on X: "Introducing Index
Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world
→ 30min of video uploads/sec
→ 16M video uploads
→ Paid $15M to date
→ 264k downloads
We're committed to spending $1B the next 12 months on data & com… / X
Post Log in Sign up Post Brett Adcock on X: "Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute" - Brett Adcock @adcock\brett Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Figure on X: "Introducing Index
Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots
To date we've crossed 264,000 app downloads and uploaded 16 million videos" / X
Post Log in Sign up Post Figure on X: "Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos" - Figure @Figure\robot Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 1:30 PM · Aug 25, 2026525.5KViews 181 345 3.6K 872 - Figure @Figure\robot Aug 25 The data needed to scale…
1:30 PM ET
Figure
1:30 PM ETFigure
Figure on X: "Introducing Index
Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots
To date we've crossed 264,000 app downloads and uploaded 16 million videos" / X
Post Log in Sign up Post Figure on X: "Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos" - Figure @Figure\robot Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 1:30 PM · Aug 25, 2026525.5KViews 181 345 3.6K 872 - Figure @Figure\robot Aug 25 The data needed to scale…
1:19 PM ET
Skild AI
1:19 PM ETSkild AI
Skild AI on X: "S1 learns new tasks like a language model.
You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment." / X
Post Log in Sign up Post Skild AI on X: "S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment." - Skild AI @SkildAI Aug 25 Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning: 00:00 View media 427 1K 7.1K 3.4M - Skild AI @SkildAI Aug 25 It can be taught extremely long-horizon tasks,…
1:19 PM ET
Skild AI
1:19 PM ETSkild AI
Skild AI on X: "Introducing S1, our new foundation model that learns from one example.
It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.
Watch S1 operate in real-time via in-context learning:" / X
Post Log in Sign up Post Skild AI on X: "Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning:" - Skild AI @SkildAI Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning: 00:00 View media 1:19 PM · Aug 25, 20263.4MViews 427…
3:43 AM ET
SUsundayrobotics
3:43 AM ETSUsundayrobotics
@maganjot79705 on X
the most interesting hardware in robotics isn't robots. @sundayrobotics built a skill capture glove so regular people can record chores. proception built a glove for dexterous hand data. UMI turned gripper data collection into a $300 tool. capture devices are the moat.
8:00 PM ET
Figure
8:00 PM ETFigure
Introducing Index: Building The World’s Largest and Most Diverse Physical Dataset
Introducing Index: Building The World’s Largest and Most Diverse Physical Dataset August 25, 2026 Today we're coming out of stealth with the most diverse robot training dataset ever built. The data needed to scale a truly general purpose robot doesn't exist on the internet - it has to come from the real world: a global sampling of physics captured across every environment on earth. For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs, with broad diversity and strict quality standards. - While in stealth, we've crossed 264,000…
Hosted By Edmond Jono Hart James (Jingxi) Xu Mene Mazarakis Angela Zhang Manfredi Bernardi Adam 134 Went Akshobhya Gupta, Hannah Tsui and 132 others Contact the Host Report Event AI 🤖🍨 Sundae Robotics 06: V-JEPA 2 & Predicting Physical Intelligence Hosted by Edmond & 6 others Aug 30 Sunday, August 30 2:00 PM - 5:00 PM PDT Register to See Address Atherton, CA Past Event This event ended 2 days ago. Welcome! To join the event, please register below. Request to Join About Event 🤖🍨 Grab a sundae and join Sundae Robotics, a private, invite-only Sunday series bringing together robotics…
1:32 PM ET
https://x.com/seohong_park
1:32 PM EThttps://x.com/seohong_park
Seohong Park on X: "Behavioral cloning mystery
https://t.co/VqxzvcfGSx
I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts!" / X
Post Log in Sign up Post Seohong Park on X: "Behavioral cloning mystery https://t.co/VqxzvcfGSx I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts!" - Seohong Park @seohong\park Behavioral cloning mystery seohong.me/blog/behaviora… I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts! View media 1:32 PM · Aug 24,…
9:15 AM ET
https://x.com/RoboPapers
9:15 AM EThttps://x.com/RoboPapers
RoboPapers on X: "Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data.
Part of the secret is that LDA-1B jointly learns forward … / X
Post Log in Sign up Post RoboPapers on X: "Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also…
9:15 AM ET
@RoboPapers
9:15 AM ET@RoboPapers
@RoboPapers on X
Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also generalizes across objects, tasks, and…
4:00 AM ET
ARAxis Robotics
4:00 AM ETARAxis Robotics
@axisrobotics on X
Axis Weekly Last week, we closed the remaining replay and runtime gaps between the browser, policy server, and physics stack — then scaled the coverage of our task generation engine (TaskGen). An articulated asset library is now in the generation path, the full RoboCasa scene grid is online, and a cleaner long- versus short-horizon task split is ready for training and distillation. Key updates: • Replay & runtime: Policy and human-trajectory replay now match the frontend path, with web runtime reaching full replay fidelity. Scene loads faster, and simulation pauses automatically when idle. •…
12:50 AM ET
@TheHumanoidHub
12:50 AM ET@TheHumanoidHub
@TheHumanoidHub on X
Generalist AI just released GEN-1.5. It might be robotics' GPT-3 moment. What is one-shot learning via in-context prompting? In the case of language models like GPT-3, including a Q&A example in the prompt before the actual question improved performance across a broad suite of language tasks. For example: Prompt: "Q: Who wrote Romeo and Juliet? A: William Shakespeare Q: Who wrote War and Peace?" [model outputs "Leo Tolstoy"] Similar capabilities are now emerging in GEN-1.5, where a short example (a few seconds of demonstration ) alongside language and sensory inputs can solve tasks the model…
12:41 PM ET
WLWorld Labs
12:41 PM ETWLWorld Labs
Bringing Marble to Life | World Labs
Nov 12, 2025A behind-the-scenes look at how Marble powered the creation of its own launch story. Bringing Marble to Life World Labs: Just Imagine - YouTube Tap to unmute World Labs: Just Imagine World Labs World Labs5.53K subscribers Overview Copy link to this section When the World Labs team set out to create Marble’s first marketing video, they made a bold decision: to build it with Marble itself. The result was a launch video created using the same technology it introduced. Over the course of a few weeks, hundreds of 3D worlds were imagined, refined, and brought into the stage pipeline.…
12:41 PM ET
PRPromise
12:41 PM ETPRPromise
From Archive to Production: A Hybrid Workflow with Marble | World Labs
Aug 12, 2026How Promise used Marble to transform archival imagery into immersive 3D environments for Harlan Coben’s Final Twist. From Archive to Production: A Hybrid Workflow with Marble Using Hybrid Production to Reconstruct Crime Scenes - YouTube Tap to unmute Using Hybrid Production to Reconstruct Crime Scenes Promise Promise573 subscribers Overview Copy link to this section For Harlan Coben's Final Twist on Paramount+ and CBS, Promise developed a hybrid production workflow that combined World Labs' Marble, AI-assisted image restoration, artist-led environment creation, and virtual…
12:01 PM ET
NVIDIA
$NVDA
12:01 PM ETNVIDIA
Cosmos 3 Post-Training in Action With Aigen and Linker Vision | Cosmos Labs - Live Broadcast by NVIDIA Robotics / X
\\ \\ REPLAY NVIDIA Robotics @NVIDIARobotics Cosmos 3 Post-Training in Action With Aigen and Linker Vision \| Cosmos Labs Scan to get the app
11:48 AM ET
@DrJimFan
11:48 AM ET@DrJimFan
@DrJimFan on X
Seeing a hype wave around GEN-1.5, and rightfully so. Lots of respect to Pete & Andy for executing so well. The secret is in the naturally repetitive motions in human-collected data. There're 2 main sources for such repetitions: (1) Symmetric patterns. Sorting, tidying, and assembling almost never finish in one motion. Open any assembly manual from IKEA, and you find most objects symmetrical. You drive one bolt, then its twin, then the next pair. Every {bolt A, bolt B} pair is a natural continuation in context, and the second instance is a free training signal that imitates the first…
11:13 AM ET
DADandy
11:13 AM ETDADandy
Jason Shuman on X: "Too many Physical AI founders are obsessed with the technology.
The team at Dandy is obsessed with the outcome.
Over the last 18 months, Dandy has become a leading Physical AI business that is delivering the highest quality crowns at industry leading prices.
And under the h… / X
Post Log in Sign up Post Jason Shuman on X: "Too many Physical AI founders are obsessed with the technology. The team at Dandy is obsessed with the outcome. Over the last 18 months, Dandy has become a leading Physical AI business that is delivering the highest quality crowns at industry leading prices. And under the hood the technology breakthroughs are insane 1\. AI Crown Design - the team built their own model from scratch to drive down the cost of design and improve design accuracy that has led to industry leading remake rates 2\. Automated Nesting Technology - Maximizes their yield on CNC…
4:14 AM ET
ARAXIS ROBOTICS
4:14 AM ETARAXIS ROBOTICS
FAQ | AXIS ROBOTICS
01Getting Started16 What do I need to use Axis Hub? A modern browser (Chrome, Firefox, Safari, or Edge) on a desktop or laptop. No GPU, no downloads, no special hardware. The physics engine — MuJoCo, compiled to WebAssembly — runs locally in your browser. How do I create an account? Sign up through Privy — you can use your email, Google account, X (Twitter), or connect an existing wallet. If you don't have a wallet, Privy creates one for you automatically. See Getting Started. What is the Axis Hub wallet? A custodial wallet created for you automatically by Privy when you sign up, so you don't…
3:33 AM ET
MEMecka
3:33 AM ETMEMecka
Mecka — The Data Engine for Physical AI
Document Index✕ - 01 Meet Mecka - 02 The Challenge - 03 The Mecka Loop - 04 Signal & Action Primitives - 05 Platform & Egoverse - 06 Contact - 07 FAQ - → Careers 01 /Telemetryreal-world sensorfusion02 /Locomotionbase rotation · J103 /Kinematicslink pose & trajectory04 /Degrees of Freedom6-axis articulation05 /Motion Capturejoint-angle telemetry06 /Contact Datagrasp · force ·tactile FIG.01 Robotics Operations Unit mecka The data and deployment layer for physical AI. FortuneWe raised $60M to power robot learning data ▶ Launch FilmWatch the Mecka launch film EgoverseAn ecosystem for robot…
8:00 PM ET
Skild AI Team
8:00 PM ETSkild AI Team
Introducing S1: In-Context Learning for Robotics | Skild AI
0:00 / 0:00 1× Introducing S1: In-Context Learning for Robotics Unseen tasks10-minute horizonsOne video promptNo post-training 13-minute read Contents Introduction Why In-Context Learning for Robotics? S1: An In-Context Learner What does S1 learn from context? The Data Engine S1 In Action Seen tasks Long-horizon unseen tasks ICL scaling laws Emergent Properties Robustness and common-sense behavior Quantifying ICL robustness to distribution shifts ICL demonstration efficiency Closing Remarks References Citation Introduction The evolution of language modeling provides a blueprint for turning…
10:00 AM ET
https://www.facebook.com/48576411181
10:00 AM EThttps://www.facebook.com/48576411181
AME Agent Swarms Quietly Rewrite the Workflow - IEEE Spectrum
IEEE.org IEEE Xplore IEEE Standards IEEE Job Site More Sites Sign In Join IEEE Return to homepage From AI Copilots to Agent Swarms Share FOR THE TECHNOLOGY INSIDER Search: Explore by topic Aerospace AI Biomedical Climate Tech Computing Consumer Electronics Energy History of Technology Robotics Semiconductors Telecommunications Transportation IEEE Spectrum FOR THE TECHNOLOGY INSIDER Topics Aerospace AI Biomedical Climate Tech Computing Consumer Electronics Energy History of Technology Robotics Semiconductors Telecommunications Transportation Sections Features News Opinion Careers DIY…
8:00 PM ET
BMW Group
8:00 PM ETBMW Group
Press-Information June 25th 2026
Press-Information June 25th 2026 BMW Group advances the use of Physical AI in production with Figure 03 project in Spartanburg - Figure AI demonstrates Figure 03 humanoid robots in new use case at BMW Group Plant Spartanburg. - Robot development runs in parallel at BMW Group Plant Spartanburg and at Figure AI. - Assembly Hall in Spartanburg features BMW iFACTORY applications in artificial intelligence and virtualization. Munich/Spartanburg, USA. BMW Group intensifies the usage of digitalization and the use of artificial intelligence (AI) in production. With so-called Physical AI, which…
4:01 PM ET
NVIDIA
$NVDA
4:01 PM ETNVIDIA
5 Physical AI infrastructure platforms shaping robotics in 2026 - The Robot Report
Facebook X LinkedIn Reddit Pinterest Share From accelerated computing and simulation to data operations, open-source tooling, validation engineering, and continuous learning, these five platforms represent distinct control points in the emerging physical AI stack. For most of the modern AI boom, infrastructure had a single center of gravity: compute. Models grew larger, training runs consumed more GPUs, and the industry organized itself around accelerators, cloud clusters, training frameworks, and developer software. That stack was sufficient when AI’s outputs were text, images, video, or…
4:01 PM ET
NVIDIA
$NVDA
4:01 PM ETNVIDIA
5 Physical AI infrastructure platforms shaping robotics in 2026 - The Robot Report
Facebook X LinkedIn Reddit Pinterest Share From accelerated computing and simulation to data operations, open-source tooling, validation engineering, and continuous learning, these five platforms represent distinct control points in the emerging physical AI stack. For most of the modern AI boom, infrastructure had a single center of gravity: compute. Models grew larger, training runs consumed more GPUs, and the industry organized itself around accelerators, cloud clusters, training frameworks, and developer software. That stack was sufficient when AI’s outputs were text, images, video, or…
10:24 AM ET
Boston Dynamics
10:24 AM ETBoston Dynamics
An Electric New Era for Atlas | Boston Dynamics
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Physical Intelligence
—Physical Intelligence
Physical-Intelligence/openpi
# openpi openpi holds open-source models and packages for robotics, published by the [Physical Intelligence team](https://www.physicalintelligence.company/). Currently, this repo contains three types of models: - the [π₀ model](https://www.physicalintelligence.company/blog/pi0), a flow-based vision-language-action model (VLA). - the [π₀-FAST model](https://www.physicalintelligence.company/research/fast), an autoregressive VLA, based on the FAST action tokenizer. - the [π₀.₅ model](https://www.physicalintelligence.company/blog/pi05), an upgraded version of π₀ with better open-world…
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SUStanford University
—SUStanford University
OpenVLA:
OpenVLA: An Open-Source Vision-Language-Action Model Moo Jin Kim∗,1 Karl Pertsch∗,1,2 Siddharth Karamcheti∗,1,3 Ted Xiao4 Ashwin Balakrishna3 Suraj Nair3 Rafael Rafailov1 Ethan Foster1 Grace Lam Pannag Sanketi4 Quan Vuong5,† Thomas Kollar3 Benjamin Burchfiel3 Russ Tedrake3,6 Dorsa Sadigh1 Sergey Levine2 Percy Liang1 Chelsea Finn1 https://openvla.github.io970k Robot Episodes ViT Llama 2 7B Base VLM OpenVLA Vision-Language-Action Model Fine-tune VLM w/ Robot Actions: Closed-Loop Robot Control Policy User: Wipe the table. OpenVLA: [ x, , Grip] = …Δ Δθ Δ Multi-Robot Control & Efficient…
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PRPromise
—PRPromise
From Archive to Production: A Hybrid Workflow with Marble | World Labs
Aug 12, 2026How Promise used Marble to transform archival imagery into immersive 3D environments for Harlan Coben’s Final Twist. From Archive to Production: A Hybrid Workflow with Marble Using Hybrid Production to Reconstruct Crime Scenes - YouTube Tap to unmute Using Hybrid Production to Reconstruct Crime Scenes Promise Promise573 subscribers Overview Copy link to this section For Harlan Coben's Final Twist on Paramount+ and CBS, Promise developed a hybrid production workflow that combined World Labs' Marble, AI-assisted image restoration, artist-led environment creation, and virtual…
π0: A Vision-Language-Action Flow Model for General Robot Control Physical Intelligence Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, Ury Zhilinsky https://physicalintelligence.company/blog/pi0 # ! 2/ . () /) , && 0'% ) 8:: ! 7 2/ . () /) ' GG ) Z)0./ GSO/, SG L ./O/ ), G mlhg / % (' (G 0 / )S/,G% y&/%,) p . n 2.G .…
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WLWorld Labs
—WLWorld Labs
Bringing Marble to Life | World Labs
Nov 12, 2025A behind-the-scenes look at how Marble powered the creation of its own launch story. Bringing Marble to Life World Labs: Just Imagine - YouTube Tap to unmute World Labs: Just Imagine World Labs World Labs5.53K subscribers Overview Copy link to this section When the World Labs team set out to create Marble’s first marketing video, they made a bold decision: to build it with Marble itself. The result was a launch video created using the same technology it introduced. Over the course of a few weeks, hundreds of 3D worlds were imagined, refined, and brought into the stage pipeline.…
Hosted By Edmond Jono Hart James (Jingxi) Xu Mene Mazarakis Angela Zhang Manfredi Bernardi Adam 134 Went Akshobhya Gupta, Hannah Tsui and 132 others Contact the Host Report Event AI 🤖🍨 Sundae Robotics 06: V-JEPA 2 & Predicting Physical Intelligence Hosted by Edmond & 6 others Aug 30 Sunday, August 30 2:00 PM - 5:00 PM PDT Register to See Address Atherton, CA Past Event This event ended 2 days ago. Welcome! To join the event, please register below. Request to Join About Event 🤖🍨 Grab a sundae and join Sundae Robotics, a private, invite-only Sunday series bringing together robotics…
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Figure
—Figure
How Figure Became the Biggest Name in Robotics | XMAQUINA DAO
Genesis Auction Wave 2! Launches June 24 Get DEUS homeDAO Portal How Figure Became the Biggest Name in Robotics Color theme: Four years, three generations of humanoids, a $39 billion valuation and now one of the largest physical AI data engines ever built. .png) August 27, 2026 Category: Physical AI Read time: 9 minutes Share This: Four years ago, Figure didn’t have a robot. Today, the company is valued at $39 billion, has built more than 1,000 humanoids, has robots working inside BMW, is preparing deployments with another major US retailer, and has developed its own AI models, manufacturing…
AgiBot World Colosseo: A Large-scale Manipulation Platform
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems Team AgiBot-World∗ Project website: https://agibot-world.com/ Code: https://github.com/OpenDriveLab/AgiBot-World1M 500k 0 No. Trajectories 7.7× BridgeDatav2 DROID A Universe of Robot Data Human-in-the-loop Versatile Scenarios Robot Platform: AgiBot G1 • 1M+ Trajectories • 217 Tasks• 3,000+ Objects • All-Purpose Sensor Setup RGBD Cameras Visuo-tactile Sensor • Dual-arm Humanoid • 6-Dof Dextrous Hand Tele- operator Robot Manual Review VLM Action Expert 46 66 78 0 30 60 90 Performance RDT…
What is true on Data?
On this layer
Figure AI22Nvidia13Skild AI12axis robotics9Google8Physical Intelligence7Unitree7Tesla6World Labs6Agibot5BMW5Hugging Face4video uploads4agentic video understanding3Agility Robotics3app downloads3SoftBank3a16z New Media2Applied Intuition2Apptronik2Booster Robotics2Dyna Robotics2Generalist2Hyundai2Lightwheel2napkin production2Pixomondo2Promise2Scale AI2The University of Hong Kong2total paid out2total payment2training data2Uber2UnitreeRobotics2Wayve2weekly active contributors2weekly active users23D inkjet printing13D reconstruction1A16z1ACE Robotics1AgiBot World platform comprises over 1 million trajectories.1Aigen1Aimalysheva1Amazon1Anthropic1Antioch1articulated assets1Atlas1Avala1average per-step success1average performance improvement of policies using the dataset.1badges for contributions in the AXIS campaign.1Bedrock1BlackRock1bmw-spartanburg-plant1Boston Dynamics1bottlenecks for scaling intelligent robots.1carbon reduction1Caterpillar1Caterpillar's operational data.1Churchill Capital XI1Columbia University1commitment on data and compute1commitment to data and compute for the next 12 months1contracted orders1crowns1Cruise1Current hours of available data for training robots.1Current throughput of valid trajectories collected per hour.1Dandy1data collection1Deep Robotics1Delivery timeline for a data center.1Demonstrations collected for the tasks.1deployments of Spot1Dexmal1Din Tai Fung1downloads1DROID dataset robot interaction data1Dyna1Dynamo Ventures1eastworlds_io1economic gains1Equinor1external funding round1factory-in-china1fal1Fetch Robotics1FieldAI1FigureAI1filtered simulation episodes1FORT Robotics1Foxglove1fremont1Geely Auto1GEN-1.5 model capabilities1General Catalyst1General Motors1Genesis AI1GGUF conversion and quantization1giga-texas1global market forecast for humanoid robots by 20271Gravis Robotics1Gritt1Groq1GrubMarket1GXO1H3 Max Turbo1Harvard University1historic crime scenes1Humanoid robots procurement1humanoid robots shipped in 20251humanoids built1Hydra-0 training1ICL scaling gains over language prompting are moderate for seen tasks.1Index uploads1Japan's postal service1JD.com1JINGDONG Industrials1lackawanna-energy-center1LG1Lightspeed1likes on Seohong Park's post1LimX Dynamics1Linker Vision1liuzhou-training-center1Lotus Cars1Madison Air1Manycore Tech1maximum number of times a user can complete the same task.1Mecka1Mercado Libre1Mercedes-Benz1Milk Road Pro1Mind Robotics1MiniMax1ministry-of-science-and-ict1MIT1Model merge process1Mostik1Munari1NBT average for RandER.1NBT average with 1% Memory Anchors removed.1NBT average with 10% Memory Anchors removed.1NBT average with 5% Memory Anchors removed.1Nemotron 3.5 Lightning on Jetson AGX Thor1Nexthop AI1Noble Machines1Number of contributors for the engine on Base.1Number of downloads of the Index platform.1Number of global contributors and collected trajectories on the platform.1number of objects in AgiBot World.1Number of tasks published.1OpenAI1payments made1payments made through Index1payments to data creators1payouts1pennsylvania-governor-josh-shapiro1performance comparison1performance improvement of the proposed policy over prior arts.1physical AI data delivered1PiPER1plant-spartanburg1Ports connecting NPUs within the rack.1Ports connecting to the network of switches.1proception1Projected growth in revenue from physical AI.1projected spending1Qatar Investment Authority1Qwen3.5-4B on Jetson Orin Nano1replies on Seohong Park's post1retweets on Seohong Park's post1Ricursive Intelligence1robot data collection1robot interaction data from the DROID dataset1robot learning economics1robot programming1robot trajectories1robot-park1Runway1S1 compared to current VLA models1S1 model capabilities1S1's performance on manipulation tasks1SambaNova1Samsung1Scale AI data delivery1Schaeffler1SDLC process rewriting1Seeed1Seeed Studio1Sequoia Capital1Sequoia's fundraising1Shanghai AI Lab1Shanghai Innovation Institute1Simulated robot bodies1south-korea-ministry-of-planning-and-budget1Spacex1spartanburg1Stanford University1status of physical AI companies1Stretch1Subscribers to SemiAnalysis.1sundayrobotics1Target valid hours of data per month from the Mobile Ego-Centric App.1teleoperation hours1the project stand not just as a piece of marketing, but as a vision statement: a world built by the very technology that made it possible.1Timeline for the plant potting task.1total memory demand with 1 million humanoid robots1total memory demand with 10 million humanoid robots1Total number of trajectories collected.1Total number of videos collected by Index.1total payments made1total production of humanoid robots1Total robotic systems procurement1Total trajectories in the engine.1Toyota Motor Manufacturing Canada1Toyota Research Institute1training hours for LDA-1B model1training needed for current VLA models to match S1's accuracy1transaction valuation1TRON 21UC Berkeley1UMI1UMI and teleoperation in robotics1Unitree上市估值1valuation1venture funding into physical AI1videos uploaded1weekly active users for data collection1Wonik Robotics1XPeng Robotics1XSquareRobot1Zhejiang University1ZiNovaLabs1Zite1国家发展改革委1开发者大会参与人数1特斯拉1
Who captures
Figure18Nvidia9axis robotics9Skild AI8@smsehy7World Labs4Google4Agibot3@stretchcloud3Physical Intelligence3Tesla2@SemiAnalysis_2The University of Hong Kong2Dyna Robotics2Unitree2Promise2@Biti88881OpenAI1Stretch1@binarybits1Munari1@rimtoln1Zite1Zhejiang University1XSquareRobot1@OperationsPLS1MiniMax1Mostik1Caterpillar1ZiNovaLabs1Anthropic1Noble Machines1eastworlds_io1@pstAsiatech1@NVIDIARobotics1UnitreeRobotics1Milk Road Pro1Sequoia Capital1Hyundai1@du_maximilian1https://x.com/poezhao06051@continuumlabs_1FigureAI1Agility Robotics1Dyna1Apptronik1Dynamo Ventures1@deepakpathak1https://x.com/huaijiangzhu1@gupta_abhinav_1https://x.com/adcock_brett1sundayrobotics1@EdmondIsARobot1https://x.com/seohong_park1https://x.com/RoboPapers1@RoboPapers1@TheHumanoidHub1@DrJimFan1Dandy1Mecka1Skild AI Team1https://www.facebook.com/485764111811BMW Group1Boston Dynamics1Stanford University1Kevin Black1Edmond1Hugging Face1
Sourced numbers
H3 Max Turbo$0.01“a $0.01/s video generation endpoint makes AI video economically viable for things like product demo generation, marketing content pipelines, and real-time synthetic data.”@stretchcloud on X
3D reconstruction25.3 units“Atlas claims mean AbsRel error of 25.3, better than specialist tools built for that task.”@stretchcloud on X
Hydra-0 training2202 hrs“It was trained on about 2,202 hours of multi-embodiment video spanning human hands, handheld grippers, single-arm robots and bimanual systems.”@techniahqrobot on X
data collection200 hrs“.@eastworlds_io is producing 200 hours of humanoid teleop data every week, making it the largest @UnitreeRobotics G1 data source outside China.”@0xconglomerate on X
Ports connecting NPUs within the rack.7 units“Seven ports are used to connect other NPUs on the same tray over flyover cables.”@SemiAnalysis_ on X
Ports connecting to the network of switches.4 units“This leaves the remaining 4 ports to connect to a network of low-radix-switches in the backplane for further intra-rack connectivity.”@SemiAnalysis_ on X
Nemotron 3.5 Lightning on Jetson AGX Thor44.8 hrs“On Jetson AGX Thor, the three training steps completed in 44.8 seconds and saved a 438 MB adapter.”Optimize Models with Unsloth | Jetson AI Lab
humanoid robots shipped in 20255,500 units“unitree shipped 5,500 humanoid robots in 2025 – more than tesla, figure ai and agility robotics combined.”@thehypedotnews on X
total production of humanoid robots6,500 units“total production exceeded 6,500 units.”@thehypedotnews on X
global market forecast for humanoid robots by 2027100,000 units“counterpoint forecasts the global market hitting 100,000 units by 2027 – 6x the 2025 numbers.”@thehypedotnews on X
total memory demand with 1 million humanoid robots$600million“that could represent approximately $600 million–$800 million in memory content, while 10 million robots could create a $6 billion–$8 billion opportunity.”@MelvinInvests on X
total memory demand with 10 million humanoid robots$6billion“while 10 million robots could create a $6 billion–$8 billion opportunity.”@MelvinInvests on X
carbon reduction1,780 units“Through smarter supply-demand matching and logistics optimisation, JINGDONG Industrials helps customers reduce unnecessary transportation and resource waste.”JD.com, Inc.
venture funding into physical AI$47.4B“Global venture funding into physical AI hit $47.4B across 521 deals in H1 2026 — nearly 4x the prior half.”@davidcao01 on X
Sequoia's fundraising$10B“Sequoia raised $10B four months after a $7B raise, explicitly reframing its thesis from "algorithms to atoms"”@davidcao01 on X
SoftBank's investment in Skild AI$1.4B“SoftBank led a $1.4B Series C into robotics firm Skild AI, tripling its valuation to $14B in seven months”@davidcao01 on X
teleoperation hours100000 hrs“Skild says the equivalent training data took to collect.”@Robot_AIsignals on X
average per-step success66 units“Skild reports 66% average per-step success on unseen tasks against 9% for a language-prompted baseline, once pre-training reaches 100,000 hours (company claim, internal evaluation).”@Robot_AIsignals on X
Index uploads16,000,000 units“Index has taken 16 million uploads from 108 countries,”@Robot_AIsignals on X
weekly active contributors44,000 units“carries 44,000+ weekly active contributors”@Robot_AIsignals on X
payments made through Index$15M“has paid out $15M — with over $1B committed to data and compute across the next twelve months (company claims).”@Robot_AIsignals on X
Number of global contributors and collected trajectories on the platform.150,000 units“As of late August 2026, the platform has surpassed 150,000 global contributors and collected over 3.7 million trajectories across 4,000+ published tasks.”Cicada Market Making on X: "https://t.co/26k9SK56mf" / X
Delivery timeline for a data center.30 hrs“Turner’s prefab arm xPL Offsite delivered a hot-aisle containment system for a 30MW rapid-deployment data center.”Dynamo Dispatch (2026/08/24) - by Santosh Sankar
weekly active users for data collection43,000 units“We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots”@adcock_brett on X
Simulated robot bodies100,000 units“They trained locomotion across 100,000 simulated robot bodies, which forces the policy to deal with different morphologies instead of just memorizing how one specific robot moves.”@0xconglomerate on X
robot data collection50 hrs“Practically takes much longer than 50-100hrs to collect 50hrs of robot data assuming people take break or make mistake too.”@deepakpathak on X
robot trajectories1 units“Every task you complete on Axis Hub is a robot trajectory — real training data for real robot policies.”@axisrobotics on X
app downloads264,000 units“264K app downloads across 108 countries”@TheHumanoidHub on X
weekly active contributors44,000 units“44K weekly active contributors”@TheHumanoidHub on X
videos uploaded16,000,000 units“16M videos uploaded, 30 minutes of video ingested every second”@TheHumanoidHub on X
commitment to data and compute for the next 12 months$1B“$1B committed to data and compute”@TheHumanoidHub on X
robot learning economics$1“you pay the enormous data bill once during foundation-model training, then amortize it across thousands of new tasks through prompting.”@rohanpaul_ai on X
S1's performance on manipulation tasks$380“one video demonstration provided roughly the benefit of 380 post-training examples!”@DeryaTR_ on X
@RoboPapers 209187717329235601230000 hrs“30,000 hours”@RoboPapers on X
articulated assets27 units“TaskGen now includes a library of 27 articulated object families (4 variants each).”@axisrobotics on X
robot programming1 units“If showing it once is enough, that changes both how fast a robot becomes useful and who can work with one.”@TheHumanoidHub on X
BMW Group production30,000 units“the Figure 02 robot supported the production of more than 30,000 BMW X3 vehicles over ten months.”Press-Information June 25th 2026
deployments of Spot1,500 units“With over 1,500 deployments, Spot is already teaching hundreds of companies how to work alongside autonomous mobile robots.”An Electric New Era for Atlas | Boston Dynamics
Training Data20000 hrs“N1.7 is pretrained on 20K hours of EgoScale human video data alongside diverse robot demonstrations.”NVIDIA/Isaac-GR00T
the project stand not just as a piece of marketing, but as a vision statement: a world built by the very technology that made it possible.1 units“The production fused artistry, engineering, and emotion into a single workflow, revealing what’s possible when imagination becomes interactive.”Bringing Marble to Life | World Labs
performance improvement of the proposed policy over prior arts.32 units“it is trained across diverse data corpus with a scalable performance of 32% gain compared to prior arts.”AgiBot World Colosseo: A Large-scale Manipulation Platform
The physical AI bottleneck is not token volume, but rich force-torque telemetry and tactile slip dynamics. Learning physical interaction requires real-world data pipelines that log contact physics at kilohertz sample rates. https://t.co/YoW0X7SyiX
11:15 PM ET
@smsehy
11:15 PM ET@smsehy
@smsehy on X
Augmenting sparse real-world physical demos with procedural simulation bridges early skill acquisition, but factory validation still demands testing across uncalibrated part tolerances and surface contamination. https://t.co/zD5ef4hsyk
11:14 PM ET
@smsehy
11:14 PM ET@smsehy
@smsehy on X
Training reinforcement learning on cached latent embeddings solves policy training throughput. The critical step for automotive manufacturing remains hard reliability optimization to eliminate rare edge-case failures during high-speed assembly. https://t.co/JZJfqC0HNe
11:11 PM ET
@smsehy
11:11 PM ET@smsehy
@smsehy on X
Crowdsourcing simulated trajectories accelerates high-level spatial priors. The engineering hurdle remains capturing high-frequency joint torque and tactile force vectors that cannot be generated without physical hardware interactions. https://t.co/PE1D6RrpiI
11:10 PM ET
@smsehy
11:10 PM ET@smsehy
@smsehy on X
255 million vehicles and global manufacturing plants already generate the physical sensor telemetry that foundation models need to learn real-world physics. Digital AI scales in datacenters, but physical AI requires industrial plants that understand ten-year hardware duty cycles. https://t.co/agvaM0yiMT
11:09 PM ET
@Biti8888
11:09 PM ET@Biti8888
@Biti8888 on X
The robot data gold rush is already happening in China. More than 90 robot gyms are launching, with 100+ robots in each center. Because the biggest bottleneck in humanoid robotics may no longer be building the robot. It’s teaching it how to work in the real world. Robots need enormous amounts of data, and an entire industry is now emerging to collect it. The gold rush isn’t just for robots. It’s for the data that will make them useful.
10:19 PM ET
AGAgibot
10:19 PM ETAGAgibot
@techniahqrobot on X
A big win for the robotics community AGIBOT just open sourced one of its most valuable real world datasets yet. @AGIBOTofficial has open-sourced AGIBOT WORLD 2026 Theme 3, a real-world dataset designed for reinforcement learning and embodied AI. The release contains 11,430 real-world robot trajectories across 14 industrial and household tasks. That includes • 1,024 successful policy rollouts • 1,369 failed policy rollouts • human in the loop corrections • external disturbances • task progress annotations • error-state annotations • human intervention data Most robot learning datasets focus…
10:14 PM ET
OPOpenAI
10:14 PM ETOPOpenAI
@DrJimFan on X
Good old days at OpenAI in 2016: an agent stares at screen pixels, moves a mouse, and books a flight on United. We called it World of Bits, inside OpenAI Universe. 10 yrs later, Astra is reincarnated in the same universe. Even the naming is astronomically correct 😆 Universe was perhaps the most ambitious AI infra project at the time, but we couldn't quite figure out how to solve it. A policy with zero prior knowledge of what a "submit" button does has to rediscover the entire internet visual lingua by trial and error. In retrospect, RL from scratch against hand-drawn, per-task "artisan"…
8:08 PM ET
STStretch
8:08 PM ETSTStretch
@stretchcloud on X
The harness problem is getting its own framework category. Tardigrade ships today: an agent harness built as typed state machine components over an immutable event log. The framing is React for harness. Each component knows its previous states. The whole harness is a pure function of the log. I keep seeing teams reach this conclusion from two directions. One: they try to build a stateful agent loop and hit the problem of debugging mid-run failures, resuming interrupted sessions, and auditing what the agent actually did and when. Two: they want to add AI behavior to existing Effect/functional…
2:58 PM ET
AGAgibot
2:58 PM ETAGAgibot
@spaceandtech_ on X
AGIBOT has officially open-sourced AGIBOT WORLD 2026 Theme 3: Reinforcement Learning, a real-world embodied AI dataset collected across expert demonstrations, autonomous policy rollouts, and human-in-the-loop corrections. It includes 11,430 real-world trajectories across 14 industrial and household tasks, capturing both successful and failed policy rollouts, along with detailed annotations for task progress, errors, disturbances, and human interventions. By learning from successes, failures, risks, and human corrections, robots can learn not only how to do it, but how to do it better.…
2:21 PM ET
@binarybits
2:21 PM ET@binarybits
@binarybits on X
This is a great illustration of why training robots manipulation tasks in simulation (middle clip) doesn't work very well. From Kai's excellent new piece on robot data. https://t.co/2ysIIvThKK https://t.co/VJDbshw5NR
2:07 PM ET
MUMunari
2:07 PM ETMUMunari
@frontrunvc on X
EARLY: @munariai - 6 followers (Robotics) Munari just launched. Operator scoring for robot training data. On @frontrunvc, the story started 47 days before the X account went live. jul 16: a tracked account follows @rmn. flagged. aug 11: flagged building. aug 19: website found. no x account yet. aug 30: a16z @speedrun SR007. sep 1: @munariai goes live. sep 4: 17 tracked accounts following the founders. a whole company, before it had a handle. this is timeline. coming soon to every company card on @frontrunvc.
1:43 PM ET
@rimtoln
1:43 PM ET@rimtoln
@rimtoln on X
EVERYONE SHOWS THE 300-AGENT SWARM. almost nobody shows what happens after 300 agents are easy to spawn turning 300 outputs into one structure is the hard part ▹ the pipeline nobody demos collect → connect → organize → unify one context graph at the end the swarm starts as hundreds of isolated research paths then sources start sharing entities links form · contradictions surface weak claims stay exposed eventually the mess collapses into one structure the whole system can reason over ▹ the reframe that's the part you usually never see the swarm is the demo the graph is the product bookmark…
1:32 PM ET
ZIZite
1:32 PM ETZIZite
@stretchcloud on X
The gap between "AI can help you build this" and "AI can actually deploy this for you" just got smaller. Zite released an MCP server that lets Claude, ChatGPT, and Cursor generate fully deployable business applications, not just code files you still have to wire up yourself. The model describes the app, Zite handles the infrastructure and deployment. This is a different category from code generation. Code generation gives you an artifact you own and maintain. Deployment-as-a-tool gives you a running application where the lifecycle management is abstracted away. The distinction matters for the…
1:19 PM ET
ZUZhejiang University
1:19 PM ETZUZhejiang University
@techniahqrobot on X
Zhejiang University just pushed long-horizon robot manipulation forward with HINT. HINT helps Vision-Language-Action models keep track of the human’s original intent across long tasks instead of losing the goal mid-sequence. On dual-arm PiPER robots, HINT raised π0.5 full-task success Fruit sorting 10% → 60% Word spelling 13.3% → 86.7% The system was also tested with unseen objects, layouts and instructions. Persistent intent tracking could become a key layer for reliable VLA systems and long-horizon Embodied AI.
12:36 PM ET
AGAgibot
12:36 PM ETAGAgibot
@Biti8888 on X
AGIBOT just open-sourced a new real-world dataset for embodied AI. @AGIBOTofficial AGIBOT WORLD 2026 Theme 3 focuses on something robots need just as much as successful demonstrations: failure and correction. The dataset includes 14 real-world tasks, 11,430 trajectories, detailed annotations of mistakes and interference, plus human-in-the-loop correction trajectories. Robots don’t improve by only seeing perfect executions. They need to learn what went wrong, how humans corrected it, and how to do better next time. https://t.co/41jAfUJ9HH
11:54 AM ET
Tesla
$TSLA
11:54 AM ETTesla
@Newsforce on X
The same activist network behind “No Kings” and other major protests is now mobilizing against data center construction and Flock license plate surveillance cameras, according to a Fox News Digital investigation and a new report. The report says organizations linked to the network have appeared across protests targeting Trump, Elon Musk, Tesla, Israel and ICE, with overlapping groups now turning their focus toward technology infrastructure and surveillance. It also highlights organizations funded by Neville Roy Singham, a Shanghai based tech billionaire whose nonprofit and media network has…
9:10 AM ET
Nvidia
$NVDA
9:10 AM ETNvidia
@CKCapitalxx on X
$NVDA just officially agreed to acquire Hugging Face for $12.93 billion. Hugging Face is where open source AI lives. Over 18 million developers, 3 million models, 500,000 datasets, and 200,000 companies using it to discover, test and deploy AI. Every open weight release lands there first. The detail most people will miss is that Nvidia was already the largest contributor to the platform. Over 500 models and 250 open datasets released there. They've been building inside this ecosystem for years, and now they own it. Here's what it opens up. >Distribution. Nvidia owns the compute. Hugging Face…
8:01 AM ET
XSXSquareRobot
8:01 AM ETXSXSquareRobot
@Biti8888 on X
The biggest bottleneck in dexterous robotics may not be the robot. It may be the data. @XSquareRobot’s TwinDEX is built around one idea: if you introduce errors during data collection, scaling the dataset won’t magically fix them later. That’s why TwinDEX matches the wearable collection device and robot hand across kinematics, contact surfaces, sensors and visual appearance. Human fingertips → wearable exoskeleton → robot fingertips. The result is high-fidelity robot-free data that can replace on-robot teleoperation for complex dexterous tasks. And with more than 5x higher collection…
5:00 AM ET
@smsehy
5:00 AM ET@smsehy
@smsehy on X
Managing private AI infrastructure requires treating data ingestion and model deployment like an industrial assembly line, where continuous telemetry monitoring and automated evaluation prevent silent production drift. https://t.co/jYGgdyTGUf
5:00 AM ET
@smsehy
5:00 AM ET@smsehy
@smsehy on X
Co-packaged optics and silicon photonics solve the physical thermal wall of high-speed copper interconnects, enabling low-latency data movement across massive distributed compute clusters without unsustainable electrical line losses. https://t.co/t5ZEAYO25J
4:13 AM ET
@OperationsPLS
4:13 AM ET@OperationsPLS
@OperationsPLS on X
Reshoring + robotics is a compounding equation. Labor cost parity via automation removes the offshore arbitrage. The real unlock is data sovereignty: domestic robot fleets generate operational data that compounds into moats. https://t.co/SPAMHZXiJF
3:47 AM ET
@stretchcloud
3:47 AM ET@stretchcloud
@stretchcloud on X
Background computer use is the feature that makes agents genuinely ambient. The agent runs while you work on something else. It clicks, types, opens apps. No babysitting. The next bottleneck is where agent clicks land: websites. Agents doing commerce, research, monitoring, or data work are spending a significant share of their compute just loading, re-reading, and re-extracting from pages they have already visited. The cost pattern I keep seeing: LLM extraction layers that call the model on every page visit. No memory of the selector. No self-healing when the site changes. The model reads the…
10:14 PM ET
@stretchcloud
10:14 PM ET@stretchcloud
@stretchcloud on X
Sodium v2 introduces a WebMCP layer. Any website can now serve structured data directly to an AI agent without HTML scraping. This is AEO (Agent Engine Optimization) in practice. The web is starting to build for agents, not just browsers. I built DeepScrape to handle exactly this: giving agents reliable, structured access to web data at inference time. https://t.co/JZYTWY8roq https://t.co/oUD6R0G9j7
8:14 PM ET
MIMiniMax
8:14 PM ETMIMiniMax
@stretchcloud on X
The pattern in AI inference is consistent: the fast-and-cheap version of a frontier model ships 2-4 weeks after the frontier version. H3 Max Turbo confirms this for video generation.H3 Max launched on August 26 at $0.08 per second of 768p output. H3 Max Turbo launches today at $0.01 per second, runs at 2x the speed, and hits the 97th percentile of H3 Max quality on evals. Seven days to a 50% cost reduction and 2x throughput improvement.The mechanism matters: fal built a separate inference engine specifically around MiniMax's H3 open-weights model architecture. They're not running H3 Max…
4:40 PM ET
@stretchcloud
4:40 PM ET@stretchcloud
@stretchcloud on X
Firecrawl's bet: the next billion users are agents. Their exact framing when they launched agent signups: "We're betting on the next 1B+ users being agents." Ask your agent to add Firecrawl, claim an API key, pull web data in seconds. The signal is correct. Agents need structured web data, not links and snippets. Web search gives pointers. What agents actually need is reliable extraction from specific pages at scale. The problem the whole category has not solved is cost. Re-reading a page with an LLM on every pass to extract data burns tokens fast. When a page changes, you re-read. When you…
4:33 PM ET
MOMostik
4:33 PM ETMOMostik
@Scobleizer on X
You have been paying a giant model to think and then to write. That second part is the expensive one. Mostik splits it. The giant model reads your problem. A small one on your own computer writes the answer. They share the thinking, not the words. 80% as good. 20 times faster. https://t.co/AD0GdYmAUg @mostik_ai @aimalysheva
3:00 PM ET
WLWorld Labs
3:00 PM ETWLWorld Labs
@stretchcloud on X
Something shifted with the World Labs Atlas announcement. We've had image generators, video generators, and 3D tools for years. Atlas does all three from a single model with the same spatial understanding. The technical description: a multimodal autoregressive diffusion transformer, pre-trained from scratch on image, video, 3D, and camera movement jointly. Not a video model with 3D features bolted on. One model, one spatial representation. What it can do: pixel-perfect camera control on generated frames up to 1440p. Video up to 60 seconds. Input a scene or image, move the camera in any…
9:43 AM ET
NVIDIA
$NVDA
9:43 AM ETNVIDIA
@techniahqrobot on X
We’re cooked if robots can start learning from bodies they don’t even have. NVIDIA Brown Columbia and Harvard researchers released Hydra-0, a world model that represents robot actions as pixel motion. It was trained on about 2,202 hours of multi-embodiment video spanning human hands, handheld grippers, single-arm robots and bimanual systems. Instead of tying an action to one specific robot body, Hydra-0 predicts how objects should move in the image. The team also tested it on a real 14-DoF YAM robot, which bent a flexible pipe from a desired object motion. This is still a controlled…
9:20 AM ET
CACaterpillar
9:20 AM ETCACaterpillar
Wall St Engine on X: "CATERPILLAR PARTNERS WITH FIELDAI ON PHYSICAL AI
$CAT is working with FieldAI to bring AI-powered autonomy and robotics into jobsites and factories, using Caterpillar’s operational data, FieldAI’s robot foundation models and NVIDIA technologies.
Early applications include au… / X
Post Log in Sign up Post Wall St Engine on X: "CATERPILLAR PARTNERS WITH FIELDAI ON PHYSICAL AI $CAT is working with FieldAI to bring AI-powered autonomy and robotics into jobsites and factories, using Caterpillar’s operational data, FieldAI’s robot foundation models and NVIDIA technologies. Early applications include autonomous inspections, real-time digital twins, risk detection and AI-driven optimization of equipment and facility operations." - Wall St Engine @wallstengine CATERPILLAR PARTNERS WITH FIELDAI ON PHYSICAL AI $CAT is working with FieldAI to bring AI-powered autonomy and…
11:48 PM ET
ARaxis robotics
11:48 PM ETARaxis robotics
Omai Leidi (3/3) on X: "Happy Wednesday frens👋
Everyone is watching the race to build better robots.
I’m starting to think the real race is the data teaching those robots how to act.
I’ve been digging into @axisrobotics, and it changed how I look at the space.
At first glance, AXIS looks like… / X
Post Log in Sign up Post Omai Leidi (3/3) on X: "Happy Wednesday frens👋 Everyone is watching the race to build better robots. I’m starting to think the real race is the data teaching those robots how to act. I’ve been digging into @axisrobotics, and it changed how I look at the space. At first glance, AXIS looks like a platform where you control simulated robots from your browser. That’s only the entry point. The bigger idea is robotic data infrastructure. Robots need experience to become useful. They need to pick things up, move objects, sort items, assemble parts, use tools, and operate in…
10:38 PM ET
Google
$GOOGL
10:38 PM ETGoogle
@OdinsDeposition on X
@dotkrueger They essentially pinpointed their long running targets today with Nvidia and Spacex CEOs both aligned on listing 10x economic gains from robotics. Prior to that 20-30% global economic gain from widespread digital AI.
10:31 PM ET
ZIZiNovaLabs
10:31 PM ETZIZiNovaLabs
LimX Dynamics on X: "Together with ZINOVA's Tool Intelligence, TRON 2 takes on increasingly complex construction workflows.
@ZiNovaLabs builds on TRON 2 to explore an innovative robotic configuration for construction, demonstrating key tasks in a scaled-down tilt-up construction workflow, includi… / X
Post Log in Sign up Post LimX Dynamics on X: "Together with ZINOVA's Tool Intelligence, TRON 2 takes on increasingly complex construction workflows. @ZiNovaLabs builds on TRON 2 to explore an innovative robotic configuration for construction, demonstrating key tasks in a scaled-down tilt-up construction workflow, including formwork assembly, multi-layer rebar placement and tying. TRON 2 serves as a modular and extensible embodied robotic platform for multi-tool, multi-step tasks across large workspaces. Its dual arms handle construction tools and materials across different orientations and…
2:21 PM ET
ANAnthropic
2:21 PM ETANAnthropic
Yuchen Jin on X: "Even if most AI labs are benchmark-maxxing now, Fable 5.1 still looks like an insane jump.
From Anthropic’s blog:
- Found a 1-in-a-million crash Millennium’s team couldn’t explain for 4–5 years
- 2x faster than Opus 5 while using half the tokens
Big if true." / X
Post Log in Sign up Post Yuchen Jin on X: "Even if most AI labs are benchmark-maxxing now, Fable 5.1 still looks like an insane jump. From Anthropic’s blog: \- Found a 1-in-a-million crash Millennium’s team couldn’t explain for 4–5 years \- 2x faster than Opus 5 while using half the tokens Big if true." - Yuchen Jin @Yuchenj\UW Even if most AI labs are benchmark-maxxing now, Fable 5.1 still looks like an insane jump. From Anthropic’s blog: \- Found a 1-in-a-million crash Millennium’s team couldn’t explain for 4–5 years \- 2x faster than Opus 5 while using half the tokens Big if true. View…
2:09 PM ET
NMNoble Machines
2:09 PM ETNMNoble Machines
From Robot Development to Deployment with Isaac GR00T & Jetson Thor - YouTube
Error 401 (Bad Request)!!1 401. That’s an error. The server cannot process the request because it is malformed. It should not be retried. That’s all we know. Back Skip navigation Search Search with your voice Sign in From Robot Development to Deployment with Isaac GR00T & Jetson Thor Tap to unmute 2x From Robot Development to Deployment with Isaac GR00T & Jetson Thor NVIDIA Omniverse 12,267 views Streamed 7d ago Copy link Info Shopping If playback doesn't begin shortly, try restarting your device. • You're signed out Videos you watch may be added to the TV's watch history and influence TV…
2:09 PM ET
Nvidia
$NVDA
2:09 PM ETNvidia
Seeed reBot Arm B601-RS: Physical AI & VLA Model Course | NVIDIA DLI Series
Warehouse China Warehouse US Warehouse Germany Warehouse The store will not work correctly in the case when cookies are disabled. US Warehouse: Enjoy FREE UNIUNI shipping on orders under $50! (Excludes XIAO & Raspberry Pi series) \\ \\ the AI Hardware Partner For Industry Products Local Warehouse Documents Customization Solution Software Support Open Claw Quick Order Account Get more with a SeeedStudio accountclear \\ \\ 1 on 1 Support \\ \\ Local Delivery \\ \\ 30-Day DOA Guarantee \\ \\ 30-Day Returns Sign In Create Account My Orders\\ Track, change, cancel Account Information\\ Update…
1:29 PM ET
Google
$GOOGL
1:29 PM ETGoogle
Google DeepMind on X: "We’re bringing agentic video understanding to our latest Gemini models.
They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵" / X
Post Log in Sign up Post Google DeepMind on X: "We’re bringing agentic video understanding to our latest Gemini models. They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵" - Google DeepMind @GoogleDeepMind We’re bringing agentic video understanding to our latest Gemini models. They can now analyze videos with better accuracy while using up to 88% fewer tokens. 🧵 View media 1:29 PM · Sep 1, 2026127.2KViews 93 132 1.3K 264 - Google DeepMind @GoogleDeepMind 21h Instead of scanning an entire file, Gemini reasons across the video’s transcript, audio, and…
1:28 PM ET
WLworld-labs
1:28 PM ETWLworld-labs
Atlas: A World Model for Spatial Intelligence | World Labs
September 1, 2026Introducing Atlas, our new omni world model for spatial intelligence. Atlas: A World Model for Spatial Intelligence World models generate, reconstruct, and simulate any possible world. They understand how worlds appear, behave, and evolve so that we can render imagined worlds for creative users, simulate the real world in high fidelity, and help robots plan actions. At World Labs, we build these general purpose world models in pursuit of spatial intelligence. Today we are introducing Atlas, our next-generation world model. Atlas is an omni model that we pretrained from…
6:30 AM ET
EAeastworlds_io
6:30 AM ETEAeastworlds_io
@0xconglomerate on X
.@eastworlds_io is producing 200 hours of humanoid teleop data every week, making it the largest @UnitreeRobotics G1 data source outside China. That might look small beside known datasets, but they combine data collection and real-world robot deployment in one operation. This means they can use what happens in deployment to refine what they collect next. So instead of just adding more hours, the data can become more targeted, efficient, and cost-effective. The tradeoff is breadth. Eastworlds’ current Unitree G1 focus gives them depth on one embodiment, but not automatically the task and scene…
10:31 PM ET
Google
$GOOGL
10:31 PM ETGoogle
smartfitguide on X: "Google DeepMind's RT-2 model treats robot actions as text tokens. By co-fine-tuning pre-trained Vision-Language Models with robotic data, it achieved nearly double the performance on novel tasks compared to its predecessor, RT-1. #AI" / X
Post Log in Sign up Post smartfitguide on X: "Google DeepMind's RT-2 model treats robot actions as text tokens. By co-fine-tuning pre-trained Vision-Language Models with robotic data, it achieved nearly double the performance on novel tasks compared to its predecessor, RT-1. \#AI" - smartfitguide @bareani21645 Google DeepMind's RT-2 model treats robot actions as text tokens. By co-fine-tuning pre-trained Vision-Language Models with robotic data, it achieved nearly double the performance on novel tasks compared to its predecessor, RT-1. #AI View media 10:31 PM · Aug 31, 20268Views Reply 1
8:36 PM ET
Skild AI
8:36 PM ETSkild AI
The Robot Report: Skild AI unveils S1 robot foundation model | AI Understanding
Back to News ProductAI Understanding briefing The Robot Report: Skild AI unveils S1 robot foundation model The Robot Report reports that Skild AI unveiled S1, a robot foundation model that the company says can learn complex tasks from a single human demonstration video and operate across multiple robot forms. By AI Understanding EditorialSeptember 1, 2026 at 12:36 AM UTCUpdatedSeptember 1, 2026 at 1:47 AM UTC6 min read Read the primary source The short version The Robot Report reports that Skild AI unveiled S1, a robot foundation model that the company says can learn complex tasks from a…
8:00 PM ET
Google
$GOOGL
8:00 PM ETGoogle
Introducing Agentic Video in Gemini
Introducing agentic video understanding with Gemini Sep 01, 2026 \| 7 min read - x.com - Facebook - LinkedIn - Mail - Copy link Our new agentic feature for video analysis cuts token consumption by up to 88%, reduces costs by up to 66%, and boosts quality by up to 7%. Rohan Doshi Senior Product Manager, Google DeepMind Mario Lučić Research Director, Google DeepMind Share - x.com - Facebook - LinkedIn - Mail - Copy link Your browser does not support the audio element. Listen to article \[\[duration\]\] minutes This content is generated by Google AI. Generative AI is experimental…
5:45 PM ET
Nvidia
$NVDA
5:45 PM ETNvidia
@a16z on X
Gavin Baker says the future for the world's biggest companies is open models and private context: "I think the future is an ensemble of models. There's a Pareto curve. No one model is going to be the best at everything." "For the global 1,000 biggest companies, you're going to take whatever the best open-source model is. I think probably in the very near future, that's going to be an Nvidia model." "Everybody says 'Well, in a world where open-source wins, who funds the training?' Chip companies can fund the training." "It's trivial to do a $50 to $100 billion training run for Jensen, but I do…
5:21 PM ET
@pstAsiatech
5:21 PM ET@pstAsiatech
国家发改委：加速具身智能在制造、医疗等领域真实场景中落地应用_财经上下游_澎湃新闻-The Paper
Seven ports are used to connect other NPUs on the same tray over flyover cables. Seven ports connect across trays in the intra-rack 2D mesh over the backplane allowing each NPU0 to connect to all the other corresponding NPU0 within the rack. This leaves the remaining 4 ports to connect to a network of low-radix-switches in the backplane for further intra-rack connectivity. (2/4)
1:39 PM ET
@NVIDIARobotics
1:39 PM ET@NVIDIARobotics
Optimize Models with Unsloth | Jetson AI Lab
🎉 Welcome to Jetson AI Lab 2.0! We've redesigned the site with curated tutorials. Looking for old content? Browse the archive → × Back to Tutorials Optimize Models with Unsloth Fine-tune, convert and deploy language models on NVIDIA Jetson with Unsloth and llama.cpp. Author Aditya Sahu Fine-tune and deploy language models directly on NVIDIA Jetson with Unsloth and JetPack 7.2. This tutorial uses memory-efficient QLoRA, exports the results to GGUF, and runs them locally. Two practical examples cover Qwen3.5-4B on Jetson Orin Nano and NVIDIA Nemotron 3.5 Lightning 30B-A3B on Jetson AGX Thor.…
12:35 PM ET
UNUnitreeRobotics
12:35 PM ETUNUnitreeRobotics
@thehypedotnews on X
unitree robots now build themselves – and train while doing it @UnitreeRobotics deployed its embodied ai model inside its own factory in china. the robots aren't repeating pre-programmed movements – they perceive the environment, understand the task and perform real manufacturing operations on the production line. this isn't just production, it's also a data engine – every task the robots perform on the line generates real-world motion data that trains the next generation of models. the factory is both the product and the training ground. unitree shipped 5,500 humanoid robots in 2025 – more…
Skip to content You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert {{ message }} Uh oh! There was an error while loading. Please reload this page. hku-sail/ StreamPI Public - Notifications You must be signed in to change notification settings - Fork\\ 4 - Star\\ 161 main 1 Branch 0 Tags Go to Branches pageGo to Tags page Go to file Code Open more actions menu Latest commit happinesslz add multi-node training…
8:57 AM ET
MRMilk Road Pro
8:57 AM ETMRMilk Road Pro
@MelvinInvests on X
The next memory supercycle is hiding inside humanoid robots (Save this). This chart estimates that each humanoid robot could contain $600–$800 or more in memory related and semiconductor components, including DRAM, HBM, NAND flash, sensors, communication chips, and other devices. If manufacturers eventually produce 1 million humanoid robots, that could represent approximately $600 million–$800 million in memory content, while 10 million robots could create a $6 billion–$8 billion opportunity. However, the larger opportunity may come from the data centers that train these robots and process…
5:11 AM ET
Figure
5:11 AM ETFigure
JD.com, Inc.
Authentic Products Delivered Today \\ AI-Powered, ESG-Embedded Industrial Supply Chain Innovation Earns JINGDONG Industrials 2026 Sedex Technoical Innovation Award \\ \\ BEIJING, 27 August 2026 — JINGDONG Industrials, a leading industrial supply chain technology and service provider under JD.com, has won the Technical Innovation Award among the 2026 Sedex Supply Chain Awards, recognising its use of AI and technical solutions to embed ESG capabilities into industrial supply chain operations. The company was also nominated for the Social Impact Award. JINGDONG Industrials was the only…
5:05 AM ET
ARAxis Robotics
5:05 AM ETARAxis Robotics
@GabrielnSpace on X
i thought community robot data would hit diminishing returns fast but Axis Dataset V1 just made that take look stupid @axisrobotics pushed π0.5 from 83.9% to 88.8% on LIBERO Plus, and performance kept climbing as training data scaled from 25% to 100% with no obvious saturation 3m trajectories later the crowd is starting to look less like users and more like a distributed robotics lab #PhysicalAI
4:00 AM ET
Figure
4:00 AM ETFigure
How Figure Became the Biggest Name in Robotics | XMAQUINA DAO
Genesis Auction Wave 2! Launches June 24 Get DEUS homeDAO Portal How Figure Became the Biggest Name in Robotics Color theme: Four years, three generations of humanoids, a $39 billion valuation and now one of the largest physical AI data engines ever built. .png) August 27, 2026 Category: Physical AI Read time: 9 minutes Share This: Four years ago, Figure didn’t have a robot. Today, the company is valued at $39 billion, has built more than 1,000 humanoids, has robots working inside BMW, is preparing deployments with another major US retailer, and has developed its own AI models, manufacturing…
3:51 AM ET
Physical Intelligence
3:51 AM ETPhysical Intelligence
[2604.15483] $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Skip to main content Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2604.15483 (cs) \Submitted on 16 Apr 2026 ( [v1), last revised 24 Apr 2026 (this version, v2)\] Title:π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities Authors: Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto, Ashwin Balakrishna, Greg Balke, Kevin Black, George Bokinsky, Shihao Cao, Thomas Charbonnier, Vedant Choudhary, Foster Collins, Ken Conley, Grace Connors, James Darpinian, Karan Dhabalia, Maitrayee Dhaka, Jared DiCarlo, Danny…
7:11 PM ET
SCSequoia Capital
7:11 PM ETSCSequoia Capital
@davidcao01 on X
Fifteen years after "software is eating the world," Marc Andreessen's own firm just admitted atoms are eating software's margins. SUMMARY This is not an a16z story. a16z's $1.1B Machine Age Fund is simply the loudest confirmation yet of a rotation already underway across Silicon Valley's largest pools of capital — Sequoia Capital , Lightspeed , General Catalyst , and SoftBank have all made comparable moves in 2026. Global venture funding into physical AI hit $47.4B across 521 deals in H1 2026 — nearly 4x the prior half. For builders and investors in robotics, autonomous systems, and…
7:11 PM ET
Skild AI
7:11 PM ETSkild AI
@Robot_AIsignals on X
Show it once. That is the entire instruction. In a post published in August, Skild AI introduced S1, a robotics foundation model prompted with a video demonstration in place of a written command. The company says the same frozen weights then performed four tasks absent from its pre-training — potting a plant, cooking pancakes, brewing pour-over coffee, assembling a kit. Skild builds Skild Brain, which it calls the first unified robotics foundation model to generalise across both tasks and robot hardware. It raised $1.4bn in January at a valuation the company puts above $14 billion (company…
4:06 PM ET
Figure
4:06 PM ETFigure
@Robot_AIsignals on X
The robot industry's data shortage just became a gig job. On 25 August Figure launched Index, an app that pays people by the minute to film themselves doing ordinary household and workplace tasks. That footage trains Helix, the AI stack running its humanoid. Figure builds the Figure 03 humanoid and the Helix system inside it, and says its Series C exceeded $1B at a $39 billion post-money valuation (company claim). Figure says Index has taken 16 million uploads from 108 countries, ingests roughly 30 minutes of footage every second, carries 44,000+ weekly active contributors and has paid out…
11:00 AM ET
Nvidia
$NVDA
11:00 AM ETNvidia
Nvidia Wants to Run the World’s Robots. China Is an Eager Customer. - WSJ
Skip to Main Content Skip to... Select - What to Read Next - Most Popular News - Most Popular Opinion DJIA\\ \\ 52766.88\\ \\ -0.79% S&P 500\\ \\ 7631.47\\ \\ -0.71% Nasdaq\\ \\ 26099.77\\ \\ -1.03% Russell 2000\\ \\ 2920.13\\ \\ -1.23% U.S. 10 Yr\\ \\ -1/32\\ \\ 4.804% VIX\\ \\ 16.34\\ \\ 9.52% Gold\\ \\ 4378.40\\ \\ -0.41% Bitcoin\\ \\ 77402.94\\ \\ 0.18% Crude Oil\\ \\ 90.77\\ \\ 0.61% Dollar Index\\ \\ 95.91\\ \\ 0.01% KBW Nasdaq Bank Index\\ \\ 183.86\\ \\ -0.84% S&P GSCI Index Spot\\ \\ 735.66\\ \\ 0.17% Advertisement This copy is for your personal, non-commercial use only. Distribution…
6:01 AM ET
HYHyundai
6:01 AM ETHYHyundai
South Korea Commits KRW 2.3 Trillion to Build Full-Stack Humanoid Robotics Ecosystem by 2030 | Humanoids Daily
Search articles Key Takeaways Show all - Dedicated Humanoid Capital: South Korea is investing KRW 2.3 trillion ($1.66 billion) through 2030 to build an indigenous, full-stack humanoid robotics industry, backed by an additional KRW 2.8 trillion ($2.0 billion) for field verification. - Aggressive Localization Targets: The plan seeks to raise domestic localization of core humanoid components from 45% to 80%, transitioning legacy auto parts manufacturers into precision robotics suppliers. - State Procurement Pipeline: The government will purchase 1,080 humanoid robots and roughly 5,000 total…
4:46 PM ET
@SemiAnalysis_
4:46 PM ET@SemiAnalysis_
SemiAnalysis ChipBook
Manage Consent To provide the best experience, we use cookies to store and/or access device information. FunctionalFunctional Always active The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network. PreferencesPreferences The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.…
1:30 PM ET
@du_maximilian
1:30 PM ET@du_maximilian
MemoryAnchors
01 · Continual Learning Challenge Data Sensitivity in Continual Robot Learning To understand Memory Anchors, we first need to take a closer look at why robot policies forget old tasks when learning new ones. Sequential Task Learning We explore the problem setting of imitation learning on tasks in sequence. Drag the slider to step through ten tasks. The horizontal axis is the learning progress, and the vertical axis is the evaluated tasks. The lighter the off-diagonal elements, the worse the forgetting. ER buffer size 0.5% (Tiny)1% (Small)5% (Medium) Training stage 12345678910 Stage 5 of 10…
11:19 AM ET
ARAxis Robotics
11:19 AM ETARAxis Robotics
Cicada Market Making on X: "https://t.co/26k9SK56mf" / X
Post Log in Sign up Post - Cicada Market Making @cicada\mm Inside Robotics and Physical AI Featuring insights from Axis Robotics In 2026, the artificial intelligence industry learned to solve the compute problem. GPUs are becoming more accessible, models are cheaper to run inference on, cloud infrastructure keeps growing. But the next wave of AI, robotics and Physical AI, has a completely different problem. It all comes down to data that simply does not exist in the volume needed. No Internet for Robots LLMs grew out of a foundation that already existed. Decades of text on the internet,…
6:51 AM ET
https://x.com/poezhao0605
6:51 AM EThttps://x.com/poezhao0605
Poe Zhao on X: "Reuters just published a special investigation on China's humanoid robot boom. I contributed analysis at three points, and together they form one argument.
Start with pricing. China's capital markets have priced humanoid technology very aggressively. Investors are essentially betti… / X
Post Log in Sign up Post Poe Zhao on X: "Reuters just published a special investigation on China's humanoid robot boom. I contributed analysis at three points, and together they form one argument. Start with pricing. China's capital markets have priced humanoid technology very aggressively. Investors are essentially betting on future general-purpose usefulness, not current capability. Then look at where the robots actually work. The economics do not support widespread deployment today. Near-term demand will stay concentrated in dangerous, repetitive and structured work. The real long-term…
5:35 AM ET
@continuumlabs_
5:35 AM ET@continuumlabs_
@continuumlabs_ on X
Physical AI has made huge progress, but scaling intelligent robots is still constrained by problems far beyond the model itself. 7 bottlenecks stand out: 1️⃣ Data: robots need massive amounts of diverse real-world data, and collecting it is slow and expensive. 2️⃣ Hardware: capable robots are still difficult to manufacture, maintain, and deploy at scale. 3️⃣ Dexterity: manipulating objects precisely remains much harder than navigating or recognizing them. 4️⃣ Reliability: real environments are full of edge cases that simulations and controlled tests don't capture. 5️⃣ Cost: expensive…
4:00 AM ET
FIFigureAI
4:00 AM ETFIFigureAI
XMAQUINA on X: "This week @FigureAI launches Index, the largest physical robot training dataset in the world.
30 minutes of video uploads per second.
16M video uploads.
$15M paid to data creators.
Figure says it's committing $1B over the next 12 months on data and compute." / X
Post Log in Sign up Post XMAQUINA on X: "This week @FigureAI launches Index, the largest physical robot training dataset in the world. 30 minutes of video uploads per second. 16M video uploads. $15M paid to data creators. Figure says it's committing $1B over the next 12 months on data and compute." - XMAQUINA @xmaquina This week @FigureAI launches Index, the largest physical robot training dataset in the world. 30 minutes of video uploads per second. 16M video uploads. $15M paid to data creators. Figure says it's committing $1B over the next 12 months on data and compute. 00:00 View media…
2:00 AM ET
ARAxis Robotics
2:00 AM ETARAxis Robotics
@axisrobotics on X
Announcing our partnership with @Dexmal_AI About Dexmal: Dexmal is a technology company focused on general-purpose embodied intelligence, committed to building intelligent, useful, and trustworthy robots. As a core data infrastructure partner, Axis is teaming up with Dexmal to empower their VLA and world models through large-scale egocentric, simulation, and real-world data production. Backed by successful enterprise data deployments, this milestone highlights the scale, precision, and production-grade reliability of Axis’s compounding physical data engine. Together, we are establishing the…
1:43 AM ET
ARAxis Robotics
1:43 AM ETARAxis Robotics
@em3kagmi on X
Many People believe Physical might be unachievable not because robots lack arms or GPUs rather, because robots do not have enough real training data. Normal AI LLM can train on text that already exists on the internet but Robots cannot. A robot needs recordings of physical actions like how an arm moves, how a grasp starts to slip, or how an object sits at a slightly different angle. That data is expensive to collect in a lab, slow to produce, and usually too clean to match the real world. @axisrobotics approaches this differently by using a distributed network to collect physical interaction…
7:05 PM ET
Tesla
$TSLA
7:05 PM ETTesla
Optimus Just Entered Production at Fremont. Here's What Changes for Tesla Investors. | The Motley Fool
$CCXI 🤖💰 CCXI / Agility Robotics Study #10✔️ The Big Question — What Could AGLT Actually Be Worth?✔️ The conclusion first: Agility’s $2.5B transaction valuation is beginning to look increasingly conservative. But we should not simply assume that CCXI at $10 = Agility at $2.5B. Once the expected post-merger share count is considered, the market-implied valuation becomes considerably higher. And this is where $15, $20, and $25 start to mean very different things. ① The Starting Point — $2.5B The Agility Robotics / Churchill Capital XI transaction was announced with: • $2.5B pre-money equity…
3:20 PM ET
DYDyna
3:20 PM ETDYDyna
@Rewkang on X
Most Physical AI companies are still doing lab demos. A key factor in our investment in Dyna last year was their world class post training expertise/results and their deployment focus. More deployments begets better data begets better models begets faster time to deployment We saw this loop play out for multimodal models like ChatGPT/Claude and autonomous driving and we're seeing it play out for robotics
2:13 PM ET
DRDyna Robotics
2:13 PM ETDRDyna Robotics
Dyna Robotics on X: "After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now.
Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaur… / X
Post Log in Sign up Post Dyna Robotics on X: "After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting…
2:13 PM ET
DRDyna Robotics
2:13 PM ETDRDyna Robotics
Not Just a Model, But a Product — DYNA
Contact11:33 AM Name Email Company Team Size 1-2021-5051-100100+ Message BusinessMediaInvestorOther Submit Sections: 01Not just a model, but a product02"Both" is our way03How Dyna-2 closed the gaps04Deployment is the eval05The deployment flywheel \[ Research \] Not Just a Model, But a Product Category: Research Author: Dyna Robotics Date: August 2026 Read: 17 min \[Sound\]\[ Fullscreen \] 1.Not just a model, but a product Today, we're really excited to share an announcement our team has been working toward over the past year: our robots have successfully crossed the ROI threshold, and Din Tai…
4:00 AM ET
ARAxis Robotics
4:00 AM ETARAxis Robotics
Axis Robotics × Booster: From Digital Twins to a Robot Data Engine
Back How task-aligned simulation, real demonstrations, and continued pretraining can make each new Booster task easier to build than the last. Robot learning has a model problem. More importantly, it has a data-operations problem. Figure — Axis’s simulation solution spans third-person, head, and wrist observations while varying appearance, materials, object layout, lighting, and scene context. The objective is to preserve task structure while varying the details that should not control the policy. Introduction For robotics companies building new embodiments—and, increasingly, for the entire…
Rhys on X: "Cool to see a bunch of new robotics foundation model releases:
π0.7 from @physical_int
GEN-1.5 from @generalistAI
S1 from @SkildAI
Feels like the GPT-3 moment for robotics. Robot Models are Few-Shot Learners!" / X
Post Log in Sign up Post Rhys on X: "Cool to see a bunch of new robotics foundation model releases: π0.7 from @physical\int GEN-1.5 from @generalistAI S1 from @SkildAI Feels like the GPT-3 moment for robotics. Robot Models are Few-Shot Learners!" - Rhys @RhysLindmark Cool to see a bunch of new robotics foundation model releases: π0.7 from @physical\int GEN-1.5 from @GeneralistAI S1 from @SkildAI Feels like the GPT-3 moment for robotics. Robot Models are Few-Shot Learners! View media View media 2:45 PM · Aug 26, 2026255Views 3 3 - Rhys @RhysLindmark Aug 26 Also damn, still so early for robot…
1:24 PM ET
Apptronik
1:24 PM ETApptronik
Humanoids are for marketing, robots are the real business
READ00% The Apollo that Apptronik showed the world at the end of June has two legs, a torso, a head and the general proportions of a five-foot-eight adult. The Apollo its paying customers keep asking for has a wheeled base instead. Apptronik, an Austin robotics company that has raised nearly a billion dollars to build a humanoid, now ships its flagship in two configurations, and the one destined for warehouses rolls. Logistics buyers, it turns out, want stability, battery life and predictability more than they want a machine that walks. The case for legs is a good one, and the company has…
12:08 PM ET
DVDynamo Ventures
12:08 PM ETDVDynamo Ventures
Dynamo Dispatch (2026/08/24) - by Santosh Sankar
Dispatch by Dynamo Ventures SubscribeSign in Discover more from Dispatch by Dynamo Ventures The signal in the noise of the industrial renaissance. Each week, we distill the headlines that matter across the physical economy along with our unique perspective and opinion. Join the thousands of founders, executives, and investors who read it. Over 4,000 subscribers Subscribe By subscribing, you agree Substack's Terms of Use, and acknowledge its Information Collection Notice and Privacy Policy. Already have an account? Sign in Dynamo Dispatch (2026/08/24) Issue 384 \| The grid, the canal, and the…
10:59 AM ET
Figure
10:59 AM ETFigure
Brett Adcock on X: "We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots
This data collection project, Index, is our answer to the data problem: the largest useful robot training dataset in the world
Extrapolate out, and this is the pretraining ne… / X
Post Log in Sign up Post Brett Adcock on X: "We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots This data collection project, Index, is our answer to the data problem: the largest useful robot training dataset in the world Extrapolate out, and this is the pretraining needed for large scale robot generalization The effort started by purchasing data from vendors but this data was scarce and really poor quality - there was simply no way to make this work correctly at scale. So we did it ourselves. It was quite a massive effort that has now…
10:59 AM ET
Figure
10:59 AM ETFigure
@adcock_brett on X
We're now over 43,000 weekly active users collecting data to train Helix, our AI model for F.03 robots This data collection project, Index, is our answer to the data problem: the largest useful robot training dataset in the world Extrapolate out, and this is the pretraining needed for large scale robot generalization The effort started by purchasing data from vendors but this data was scarce and really poor quality - there was simply no way to make this work correctly at scale. So we did it ourselves. It was quite a massive effort that has now resulted in a Figure-owned data pipeline that is…
10:02 AM ET
Skild AI
10:02 AM ETSkild AI
@ThomasSmale on X
$14B valuation. Pre-revenue. A handful of customers. That is Skild AI today. And it is not the outlier. Physical Intelligence: $11B Generalist: $2B to $3B in months, $600M raised Skild AI: $14B Three companies. Billions in capital. Almost no commercial traction between them. The thesis is simple. Someone builds the foundation model for robotics the way OpenAI built one for language. Find the winner early, own the platform forever. Here is the problem. LLMs were trained on trillions of words scraped from the open internet. That dataset already existed, and it was free. No equivalent exists for…
9:30 AM ET
Unitree
9:30 AM ETUnitree
Robot brain builders are pushing out of their GPT-2 era | TechCrunch
Checking your Browser… Verifying... Stuck? Troubleshoot Success! Verification failed Troubleshoot Verification expired Refresh Verification expired Refresh Troubleshoot Cloudflare, opens in a new tab Privacy • Help Skip to content Image Credits: Kiyoshi Ota/Bloomberg / Getty Images Robotics Share on FacebookShare on XShare on LinkedInShare on RedditShare over EmailCopy Share Link Robot brain builders are pushing out of their GPT-2 era Tim Fernholz 6:30 AM PDT · August 26, 2026 Share on FacebookShare on XShare on LinkedInShare on RedditShare over EmailCopy Share Link Physical AI is one of the…
6:09 AM ET
Skild
6:09 AM ETSkild
@0xconglomerate on X
Is it really possible for a robot to learn a new task from one human video, with zero fine-tuning? I’ve read a lot of hype around Skild’s S1, with some already calling it robotics’ “ChatGPT moment.” That sounded a bit absurd to me, so I looked into what’s actually going on 👇 ➦ What is @SkildAI actually claiming? I’ve written enough marketing material for AI, robotics, and tech companies that I’m usually skeptical when something sounds too good to be true. If you only saw the announcement post, you could easily think the robot just watches one human video and somehow learns the task from that…
2:33 AM ET
@deepakpathak
2:33 AM ET@deepakpathak
@deepakpathak on X
@JieWang_ZJUI These are 5-10min long tasks, ~500 demos needed for VLAs. Practically takes much longer than 50-100hrs to collect 50hrs of robot data assuming people take break or make mistake too.
2:18 AM ET
ARAxis Robotics
2:18 AM ETARAxis Robotics
@axisrobotics on X
2/ What you're actually doing Axis is the compounding data engine for physical AI. Every task you complete on Axis Hub is a robot trajectory — real training data for real robot policies. You do the demonstration, we verify it, you sign it on-chain. Your contribution becomes a permanent record. Learn more:
7:06 PM ET
Figure
7:06 PM ETFigure
@TheHumanoidHub on X
Figure takes vertical integration to another level. Figure built its own egocentric data collection pipeline. Index is a crowdsourcing platform where anyone can collect data. - 264K app downloads across 108 countries - 44K weekly active contributors - 16M videos uploaded, 30 minutes of video ingested every second - $15M already paid out to contributors Figure says data vendors couldn't hit the throughput, diversity, or quality bar, so they built the pipeline themselves. Next 12 months: $1B committed to data and compute.
5:27 PM ET
https://x.com/huaijiangzhu
5:27 PM EThttps://x.com/huaijiangzhu
huaijiang on X: "ok, so if i understood this correctly, the “in-context learning” ability was essentially baked into the model at training time. roughly speaking, the policy is trained to do something like:
(demo video, current obs) → action
this is quite different from in-context learning in llm… / X
Post Log in Sign up Post huaijiang on X: "ok, so if i understood this correctly, the “in-context learning” ability was essentially baked into the model at training time. roughly speaking, the policy is trained to do something like: (demo video, current obs) → action this is quite different from in-context learning in llms, where the model is simply trained on next-token prediction over arbitrary context. fwiw, this is pretty clever because it could also solve another problem: they could pair an egocentric video-only demonstration with a UMI/teleop trajectory for the same task. the target…
2:05 PM ET
Skild
2:05 PM ETSkild
@rohanpaul_ai on X
Robotics is more and more getting close to get its version of in-context learning. Skild just released S1, a robot foundation model that uses video demonstrations to define tasks instead of language instructions. Give S1 1 human video showing a long, multi-step task, and the robot executes it straight away. No retraining. No fine-tuning. If this scales, you pay the enormous data bill once during foundation-model training, then amortize it across thousands of new tasks through prompting. That could change the economics of robot learning completely.
2:04 PM ET
Skild
2:04 PM ETSkild
@DeryaTR_ on X
This is super exciting advance in robotics AI! I believe S1 is the most impressive robot foundation model I had seen! S1, is a robot foundation model built to learn new manipulation tasks through in-context prompting In Skild’s internal benchmarks, S1 achieved a 66% step-success rate on unseen tasks versus 9% for a language-prompted model, while one video demonstration provided roughly the benefit of 380 post-training examples!
1:54 PM ET
@gupta_abhinav_
1:54 PM ET@gupta_abhinav_
@gupta_abhinav_ on X
@0fir0z @deepakpathak In the silicon valley world the race for robotics companies is to be similar to LLMs. But actually robotics needs many more concepts from computer vision. We are proud to say S1 is using so many of our past efforts in computer vision CC: @JitendraMalikCV
1:38 PM ET
Figure
1:38 PM ETFigure
Figure on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute
Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" / X
Post Log in Sign up Post Figure on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure…
1:36 PM ET
Figure
1:36 PM ETFigure
Figure on X: "By the numbers:
→ While in stealth, we crossed 264,000 app downloads
→ Uploading over 30 min of video very second
→ Over 16M video uploads
→ Paid out $15M to date
Weekly active users is now over 43,000" / X
Post Log in Sign up Post Figure on X: "By the numbers: → While in stealth, we crossed 264,000 app downloads → Uploading over 30 min of video very second → Over 16M video uploads → Paid out $15M to date Weekly active users is now over 43,000" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure @Figure\robot Aug 25 The data needed to scale a truly general purpose robots…
1:35 PM ET
Figure
1:35 PM ETFigure
Figure on X: "We've now collected data in 108 countries to date
Here's a global map of our collection, now updating live on our website, each point of light represents a real world contributions to our dataset" / X
Post Log in Sign up Post Figure on X: "We've now collected data in 108 countries to date Here's a global map of our collection, now updating live on our website, each point of light represents a real world contributions to our dataset" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure @Figure\robot Aug 25 The data needed to scale a truly general purpose robots…
1:35 PM ET
https://x.com/adcock_brett
1:35 PM EThttps://x.com/adcock_brett
Brett Adcock on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute
Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" / X
Post Log in Sign up Post Brett Adcock on X: "We are now on a path to 100x, and are committed to spend over $1B the next 12 months on data and compute Index is laying the groundwork for ordering robots as a service. Today, you have people coming to help clean your house; eventually, a robot will do everything for you" - Brett Adcock @adcock\brett Aug 25 Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next…
1:34 PM ET
Figure
1:34 PM ETFigure
Figure on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world
For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" / X
Post Log in Sign up Post Figure on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" - Figure @Figure\robot Aug 25 Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 181 345 3.6K 525K - Figure…
1:33 PM ET
Figure
1:33 PM ETFigure
Brett Adcock on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world
For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" / X
Post Log in Sign up Post Brett Adcock on X: "The data needed to scale a truly general purpose robots doesn't exist on the internet - it has to come from the real world For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs. Today, we’re coming out of stealth" - Brett Adcock @adcock\brett Aug 25 Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Brett Adcock on X: "Introducing Index
Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world
→ 30min of video uploads/sec
→ 16M video uploads
→ Paid $15M to date
→ 264k downloads
We're committed to spending $1B the next 12 months on data & com… / X
Post Log in Sign up Post Brett Adcock on X: "Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute" - Brett Adcock @adcock\brett Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Brett Adcock on X: "Introducing Index
Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world
→ 30min of video uploads/sec
→ 16M video uploads
→ Paid $15M to date
→ 264k downloads
We're committed to spending $1B the next 12 months on data & com… / X
Post Log in Sign up Post Brett Adcock on X: "Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute" - Brett Adcock @adcock\brett Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Brett Adcock on X: "Introducing Index
Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world
→ 30min of video uploads/sec
→ 16M video uploads
→ Paid $15M to date
→ 264k downloads
We're committed to spending $1B the next 12 months on data & com… / X
Post Log in Sign up Post Brett Adcock on X: "Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute" - Brett Adcock @adcock\brett Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the…
1:30 PM ET
Figure
1:30 PM ETFigure
Figure on X: "Introducing Index
Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots
To date we've crossed 264,000 app downloads and uploaded 16 million videos" / X
Post Log in Sign up Post Figure on X: "Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos" - Figure @Figure\robot Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 1:30 PM · Aug 25, 2026525.5KViews 181 345 3.6K 872 - Figure @Figure\robot Aug 25 The data needed to scale…
1:30 PM ET
Figure
1:30 PM ETFigure
Figure on X: "Introducing Index
Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots
To date we've crossed 264,000 app downloads and uploaded 16 million videos" / X
Post Log in Sign up Post Figure on X: "Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos" - Figure @Figure\robot Introducing Index Today we're coming out of stealth with the largest and most diverse robot dataset to scale general purpose robots To date we've crossed 264,000 app downloads and uploaded 16 million videos 00:00 View media 1:30 PM · Aug 25, 2026525.5KViews 181 345 3.6K 872 - Figure @Figure\robot Aug 25 The data needed to scale…
1:19 PM ET
Skild AI
1:19 PM ETSkild AI
Skild AI on X: "S1 learns new tasks like a language model.
You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment." / X
Post Log in Sign up Post Skild AI on X: "S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment." - Skild AI @SkildAI Aug 25 Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning: 00:00 View media 427 1K 7.1K 3.4M - Skild AI @SkildAI Aug 25 It can be taught extremely long-horizon tasks,…
1:19 PM ET
Skild AI
1:19 PM ETSkild AI
Skild AI on X: "Introducing S1, our new foundation model that learns from one example.
It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.
Watch S1 operate in real-time via in-context learning:" / X
Post Log in Sign up Post Skild AI on X: "Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning:" - Skild AI @SkildAI Introducing S1, our new foundation model that learns from one example. It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning. Watch S1 operate in real-time via in-context learning: 00:00 View media 1:19 PM · Aug 25, 20263.4MViews 427…
3:43 AM ET
SUsundayrobotics
3:43 AM ETSUsundayrobotics
@maganjot79705 on X
the most interesting hardware in robotics isn't robots. @sundayrobotics built a skill capture glove so regular people can record chores. proception built a glove for dexterous hand data. UMI turned gripper data collection into a $300 tool. capture devices are the moat.
8:00 PM ET
Figure
8:00 PM ETFigure
Introducing Index: Building The World’s Largest and Most Diverse Physical Dataset
Introducing Index: Building The World’s Largest and Most Diverse Physical Dataset August 25, 2026 Today we're coming out of stealth with the most diverse robot training dataset ever built. The data needed to scale a truly general purpose robot doesn't exist on the internet - it has to come from the real world: a global sampling of physics captured across every environment on earth. For the last 4 months, we’ve been building a Figure-exclusive pipeline to scale data collection at higher throughputs, with broad diversity and strict quality standards. - While in stealth, we've crossed 264,000…
Hosted By Edmond Jono Hart James (Jingxi) Xu Mene Mazarakis Angela Zhang Manfredi Bernardi Adam 134 Went Akshobhya Gupta, Hannah Tsui and 132 others Contact the Host Report Event AI 🤖🍨 Sundae Robotics 06: V-JEPA 2 & Predicting Physical Intelligence Hosted by Edmond & 6 others Aug 30 Sunday, August 30 2:00 PM - 5:00 PM PDT Register to See Address Atherton, CA Past Event This event ended 2 days ago. Welcome! To join the event, please register below. Request to Join About Event 🤖🍨 Grab a sundae and join Sundae Robotics, a private, invite-only Sunday series bringing together robotics…
1:32 PM ET
https://x.com/seohong_park
1:32 PM EThttps://x.com/seohong_park
Seohong Park on X: "Behavioral cloning mystery
https://t.co/VqxzvcfGSx
I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts!" / X
Post Log in Sign up Post Seohong Park on X: "Behavioral cloning mystery https://t.co/VqxzvcfGSx I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts!" - Seohong Park @seohong\park Behavioral cloning mystery seohong.me/blog/behaviora… I wrote a new blog post about "mysteries" in behavioral cloning that appear with real-world robot data (e.g., overfitting is "good"). I also tried to demystify them and shared my thoughts! View media 1:32 PM · Aug 24,…
9:15 AM ET
https://x.com/RoboPapers
9:15 AM EThttps://x.com/RoboPapers
RoboPapers on X: "Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data.
Part of the secret is that LDA-1B jointly learns forward … / X
Post Log in Sign up Post RoboPapers on X: "Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also…
9:15 AM ET
@RoboPapers
9:15 AM ET@RoboPapers
@RoboPapers on X
Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also generalizes across objects, tasks, and…
4:00 AM ET
ARAxis Robotics
4:00 AM ETARAxis Robotics
@axisrobotics on X
Axis Weekly Last week, we closed the remaining replay and runtime gaps between the browser, policy server, and physics stack — then scaled the coverage of our task generation engine (TaskGen). An articulated asset library is now in the generation path, the full RoboCasa scene grid is online, and a cleaner long- versus short-horizon task split is ready for training and distillation. Key updates: • Replay & runtime: Policy and human-trajectory replay now match the frontend path, with web runtime reaching full replay fidelity. Scene loads faster, and simulation pauses automatically when idle. •…
12:50 AM ET
@TheHumanoidHub
12:50 AM ET@TheHumanoidHub
@TheHumanoidHub on X
Generalist AI just released GEN-1.5. It might be robotics' GPT-3 moment. What is one-shot learning via in-context prompting? In the case of language models like GPT-3, including a Q&A example in the prompt before the actual question improved performance across a broad suite of language tasks. For example: Prompt: "Q: Who wrote Romeo and Juliet? A: William Shakespeare Q: Who wrote War and Peace?" [model outputs "Leo Tolstoy"] Similar capabilities are now emerging in GEN-1.5, where a short example (a few seconds of demonstration ) alongside language and sensory inputs can solve tasks the model…
12:41 PM ET
WLWorld Labs
12:41 PM ETWLWorld Labs
Bringing Marble to Life | World Labs
Nov 12, 2025A behind-the-scenes look at how Marble powered the creation of its own launch story. Bringing Marble to Life World Labs: Just Imagine - YouTube Tap to unmute World Labs: Just Imagine World Labs World Labs5.53K subscribers Overview Copy link to this section When the World Labs team set out to create Marble’s first marketing video, they made a bold decision: to build it with Marble itself. The result was a launch video created using the same technology it introduced. Over the course of a few weeks, hundreds of 3D worlds were imagined, refined, and brought into the stage pipeline.…
12:41 PM ET
PRPromise
12:41 PM ETPRPromise
From Archive to Production: A Hybrid Workflow with Marble | World Labs
Aug 12, 2026How Promise used Marble to transform archival imagery into immersive 3D environments for Harlan Coben’s Final Twist. From Archive to Production: A Hybrid Workflow with Marble Using Hybrid Production to Reconstruct Crime Scenes - YouTube Tap to unmute Using Hybrid Production to Reconstruct Crime Scenes Promise Promise573 subscribers Overview Copy link to this section For Harlan Coben's Final Twist on Paramount+ and CBS, Promise developed a hybrid production workflow that combined World Labs' Marble, AI-assisted image restoration, artist-led environment creation, and virtual…
12:01 PM ET
NVIDIA
$NVDA
12:01 PM ETNVIDIA
Cosmos 3 Post-Training in Action With Aigen and Linker Vision | Cosmos Labs - Live Broadcast by NVIDIA Robotics / X
\\ \\ REPLAY NVIDIA Robotics @NVIDIARobotics Cosmos 3 Post-Training in Action With Aigen and Linker Vision \| Cosmos Labs Scan to get the app
11:48 AM ET
@DrJimFan
11:48 AM ET@DrJimFan
@DrJimFan on X
Seeing a hype wave around GEN-1.5, and rightfully so. Lots of respect to Pete & Andy for executing so well. The secret is in the naturally repetitive motions in human-collected data. There're 2 main sources for such repetitions: (1) Symmetric patterns. Sorting, tidying, and assembling almost never finish in one motion. Open any assembly manual from IKEA, and you find most objects symmetrical. You drive one bolt, then its twin, then the next pair. Every {bolt A, bolt B} pair is a natural continuation in context, and the second instance is a free training signal that imitates the first…
11:13 AM ET
DADandy
11:13 AM ETDADandy
Jason Shuman on X: "Too many Physical AI founders are obsessed with the technology.
The team at Dandy is obsessed with the outcome.
Over the last 18 months, Dandy has become a leading Physical AI business that is delivering the highest quality crowns at industry leading prices.
And under the h… / X
Post Log in Sign up Post Jason Shuman on X: "Too many Physical AI founders are obsessed with the technology. The team at Dandy is obsessed with the outcome. Over the last 18 months, Dandy has become a leading Physical AI business that is delivering the highest quality crowns at industry leading prices. And under the hood the technology breakthroughs are insane 1\. AI Crown Design - the team built their own model from scratch to drive down the cost of design and improve design accuracy that has led to industry leading remake rates 2\. Automated Nesting Technology - Maximizes their yield on CNC…
4:14 AM ET
ARAXIS ROBOTICS
4:14 AM ETARAXIS ROBOTICS
FAQ | AXIS ROBOTICS
01Getting Started16 What do I need to use Axis Hub? A modern browser (Chrome, Firefox, Safari, or Edge) on a desktop or laptop. No GPU, no downloads, no special hardware. The physics engine — MuJoCo, compiled to WebAssembly — runs locally in your browser. How do I create an account? Sign up through Privy — you can use your email, Google account, X (Twitter), or connect an existing wallet. If you don't have a wallet, Privy creates one for you automatically. See Getting Started. What is the Axis Hub wallet? A custodial wallet created for you automatically by Privy when you sign up, so you don't…
3:33 AM ET
MEMecka
3:33 AM ETMEMecka
Mecka — The Data Engine for Physical AI
Document Index✕ - 01 Meet Mecka - 02 The Challenge - 03 The Mecka Loop - 04 Signal & Action Primitives - 05 Platform & Egoverse - 06 Contact - 07 FAQ - → Careers 01 /Telemetryreal-world sensorfusion02 /Locomotionbase rotation · J103 /Kinematicslink pose & trajectory04 /Degrees of Freedom6-axis articulation05 /Motion Capturejoint-angle telemetry06 /Contact Datagrasp · force ·tactile FIG.01 Robotics Operations Unit mecka The data and deployment layer for physical AI. FortuneWe raised $60M to power robot learning data ▶ Launch FilmWatch the Mecka launch film EgoverseAn ecosystem for robot…
8:00 PM ET
Skild AI Team
8:00 PM ETSkild AI Team
Introducing S1: In-Context Learning for Robotics | Skild AI
0:00 / 0:00 1× Introducing S1: In-Context Learning for Robotics Unseen tasks10-minute horizonsOne video promptNo post-training 13-minute read Contents Introduction Why In-Context Learning for Robotics? S1: An In-Context Learner What does S1 learn from context? The Data Engine S1 In Action Seen tasks Long-horizon unseen tasks ICL scaling laws Emergent Properties Robustness and common-sense behavior Quantifying ICL robustness to distribution shifts ICL demonstration efficiency Closing Remarks References Citation Introduction The evolution of language modeling provides a blueprint for turning…
10:00 AM ET
https://www.facebook.com/48576411181
10:00 AM EThttps://www.facebook.com/48576411181
AME Agent Swarms Quietly Rewrite the Workflow - IEEE Spectrum
IEEE.org IEEE Xplore IEEE Standards IEEE Job Site More Sites Sign In Join IEEE Return to homepage From AI Copilots to Agent Swarms Share FOR THE TECHNOLOGY INSIDER Search: Explore by topic Aerospace AI Biomedical Climate Tech Computing Consumer Electronics Energy History of Technology Robotics Semiconductors Telecommunications Transportation IEEE Spectrum FOR THE TECHNOLOGY INSIDER Topics Aerospace AI Biomedical Climate Tech Computing Consumer Electronics Energy History of Technology Robotics Semiconductors Telecommunications Transportation Sections Features News Opinion Careers DIY…
8:00 PM ET
BMW Group
8:00 PM ETBMW Group
Press-Information June 25th 2026
Press-Information June 25th 2026 BMW Group advances the use of Physical AI in production with Figure 03 project in Spartanburg - Figure AI demonstrates Figure 03 humanoid robots in new use case at BMW Group Plant Spartanburg. - Robot development runs in parallel at BMW Group Plant Spartanburg and at Figure AI. - Assembly Hall in Spartanburg features BMW iFACTORY applications in artificial intelligence and virtualization. Munich/Spartanburg, USA. BMW Group intensifies the usage of digitalization and the use of artificial intelligence (AI) in production. With so-called Physical AI, which…
4:01 PM ET
NVIDIA
$NVDA
4:01 PM ETNVIDIA
5 Physical AI infrastructure platforms shaping robotics in 2026 - The Robot Report
Facebook X LinkedIn Reddit Pinterest Share From accelerated computing and simulation to data operations, open-source tooling, validation engineering, and continuous learning, these five platforms represent distinct control points in the emerging physical AI stack. For most of the modern AI boom, infrastructure had a single center of gravity: compute. Models grew larger, training runs consumed more GPUs, and the industry organized itself around accelerators, cloud clusters, training frameworks, and developer software. That stack was sufficient when AI’s outputs were text, images, video, or…
4:01 PM ET
NVIDIA
$NVDA
4:01 PM ETNVIDIA
5 Physical AI infrastructure platforms shaping robotics in 2026 - The Robot Report
Facebook X LinkedIn Reddit Pinterest Share From accelerated computing and simulation to data operations, open-source tooling, validation engineering, and continuous learning, these five platforms represent distinct control points in the emerging physical AI stack. For most of the modern AI boom, infrastructure had a single center of gravity: compute. Models grew larger, training runs consumed more GPUs, and the industry organized itself around accelerators, cloud clusters, training frameworks, and developer software. That stack was sufficient when AI’s outputs were text, images, video, or…
10:24 AM ET
Boston Dynamics
10:24 AM ETBoston Dynamics
An Electric New Era for Atlas | Boston Dynamics
We value your privacy We use cookies to enhance your browsing experience, serve personalized ads or content, and analyze our traffic. By clicking "Accept All", you consent to our use of cookies. CustomizeReject AllAccept All Customize Consent Preferences We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more…
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Physical Intelligence
—Physical Intelligence
Physical-Intelligence/openpi
# openpi openpi holds open-source models and packages for robotics, published by the [Physical Intelligence team](https://www.physicalintelligence.company/). Currently, this repo contains three types of models: - the [π₀ model](https://www.physicalintelligence.company/blog/pi0), a flow-based vision-language-action model (VLA). - the [π₀-FAST model](https://www.physicalintelligence.company/research/fast), an autoregressive VLA, based on the FAST action tokenizer. - the [π₀.₅ model](https://www.physicalintelligence.company/blog/pi05), an upgraded version of π₀ with better open-world…
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SUStanford University
—SUStanford University
OpenVLA:
OpenVLA: An Open-Source Vision-Language-Action Model Moo Jin Kim∗,1 Karl Pertsch∗,1,2 Siddharth Karamcheti∗,1,3 Ted Xiao4 Ashwin Balakrishna3 Suraj Nair3 Rafael Rafailov1 Ethan Foster1 Grace Lam Pannag Sanketi4 Quan Vuong5,† Thomas Kollar3 Benjamin Burchfiel3 Russ Tedrake3,6 Dorsa Sadigh1 Sergey Levine2 Percy Liang1 Chelsea Finn1 https://openvla.github.io970k Robot Episodes ViT Llama 2 7B Base VLM OpenVLA Vision-Language-Action Model Fine-tune VLM w/ Robot Actions: Closed-Loop Robot Control Policy User: Wipe the table. OpenVLA: [ x, , Grip] = …Δ Δθ Δ Multi-Robot Control & Efficient…
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PRPromise
—PRPromise
From Archive to Production: A Hybrid Workflow with Marble | World Labs
Aug 12, 2026How Promise used Marble to transform archival imagery into immersive 3D environments for Harlan Coben’s Final Twist. From Archive to Production: A Hybrid Workflow with Marble Using Hybrid Production to Reconstruct Crime Scenes - YouTube Tap to unmute Using Hybrid Production to Reconstruct Crime Scenes Promise Promise573 subscribers Overview Copy link to this section For Harlan Coben's Final Twist on Paramount+ and CBS, Promise developed a hybrid production workflow that combined World Labs' Marble, AI-assisted image restoration, artist-led environment creation, and virtual…
π0: A Vision-Language-Action Flow Model for General Robot Control Physical Intelligence Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, Ury Zhilinsky https://physicalintelligence.company/blog/pi0 # ! 2/ . () /) , && 0'% ) 8:: ! 7 2/ . () /) ' GG ) Z)0./ GSO/, SG L ./O/ ), G mlhg / % (' (G 0 / )S/,G% y&/%,) p . n 2.G .…
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WLWorld Labs
—WLWorld Labs
Bringing Marble to Life | World Labs
Nov 12, 2025A behind-the-scenes look at how Marble powered the creation of its own launch story. Bringing Marble to Life World Labs: Just Imagine - YouTube Tap to unmute World Labs: Just Imagine World Labs World Labs5.53K subscribers Overview Copy link to this section When the World Labs team set out to create Marble’s first marketing video, they made a bold decision: to build it with Marble itself. The result was a launch video created using the same technology it introduced. Over the course of a few weeks, hundreds of 3D worlds were imagined, refined, and brought into the stage pipeline.…
Hosted By Edmond Jono Hart James (Jingxi) Xu Mene Mazarakis Angela Zhang Manfredi Bernardi Adam 134 Went Akshobhya Gupta, Hannah Tsui and 132 others Contact the Host Report Event AI 🤖🍨 Sundae Robotics 06: V-JEPA 2 & Predicting Physical Intelligence Hosted by Edmond & 6 others Aug 30 Sunday, August 30 2:00 PM - 5:00 PM PDT Register to See Address Atherton, CA Past Event This event ended 2 days ago. Welcome! To join the event, please register below. Request to Join About Event 🤖🍨 Grab a sundae and join Sundae Robotics, a private, invite-only Sunday series bringing together robotics…
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Figure
—Figure
How Figure Became the Biggest Name in Robotics | XMAQUINA DAO
Genesis Auction Wave 2! Launches June 24 Get DEUS homeDAO Portal How Figure Became the Biggest Name in Robotics Color theme: Four years, three generations of humanoids, a $39 billion valuation and now one of the largest physical AI data engines ever built. .png) August 27, 2026 Category: Physical AI Read time: 9 minutes Share This: Four years ago, Figure didn’t have a robot. Today, the company is valued at $39 billion, has built more than 1,000 humanoids, has robots working inside BMW, is preparing deployments with another major US retailer, and has developed its own AI models, manufacturing…