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32,000+ hours of open robot data 🤖 ANYONE can get started training their own robotics models!

Vai Viswanathan

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13,368 次观看 • 2 个月前 •via X (Twitter)

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We have nearly 40,000 hours of open robotics data on Check it out and train your own robotics policy or world model

We have nearly 40,000 hours of open robotics data on Check it out and train your own robotics policy or world model

Vai Viswanathan

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Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗 #Robotics #EmbodiedAI #FoundationModels #RobotLearning #XiaomiAI #XiaomiRobotics

Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗 #Robotics #EmbodiedAI #FoundationModels #RobotLearning #XiaomiAI #XiaomiRobotics

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22,568 次观看 • 1 个月前

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

RoboPapers

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.Sureform (YC X25) connects real-world workplaces with robotics labs to collect task-specific training data for robot foundation models. Congrats on the launch, Ananth Kashyap!

.Sureform (YC X25) connects real-world workplaces with robotics labs to collect task-specific training data for robot foundation models. Congrats on the launch, Ananth Kashyap!

Y Combinator

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The First 100 begins now. Verify Quality is live on PrismaX. For the first time, anyone can score the robot training data that models learn from, earn points, and compete to become one of The First 100. Better data. Better models. The standard starts with you.

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1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group Zhuoyuan Wu! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: 🌐 Project: 💻 Code: 🤗 Robot data: 🤗 Human data: 🤗 Weights: #Robotics #WorldModels #AI #OpenSource

1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group Zhuoyuan Wu! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: 🌐 Project: 💻 Code: 🤗 Robot data: 🤗 Human data: 🤗 Weights: #Robotics #WorldModels #AI #OpenSource

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꧁IP꧂ encompasses all data used to fuel AI models. On Story, anyone can register open source AI models, data sets, and fine tune their models. AI cannot exist without IP, and Story makes IP programmable.

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The DATA Foundation

155,437 次观看 • 1 年前

Austin-based robotics startup Apptronik has unveiled Apollo 2, the latest version of its humanoid robot. Apollo 2 comes in both bipedal and wheeled versions and performs real-world tasks while collecting training data. Apptronik is using this data with Google DeepMind to train Gemini Robotics AI models, making Apollo smarter and more capable.

Austin-based robotics startup Apptronik has unveiled Apollo 2, the latest version of its humanoid robot. Apollo 2 comes in both bipedal and wheeled versions and performs real-world tasks while collecting training data. Apptronik is using this data with Google DeepMind to train Gemini Robotics AI models, making Apollo smarter and more capable.

Space and Technology

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Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog →

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LeRobot

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Training data collection is one of the biggest challenges in #Robotics. We're solving it with #NVIDIAIsaac GR00T-Dreams. By using #NVIDIACosmos world foundation models and generative AI, developers can create vast, realistic synthetic data at scale. Benefits: ✔️Cheaper & faster data collection ✔️Smarter robots in new environments ✔️ No more teleoperation bottlenecks ✔️ Small teams = massive impact Get Started 🔗 #GTCParis #VivaTech

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NVIDIA Robotics

17,037 次观看 • 1 年前

Co-training has become a key part of the recipe for training large robotics models; it means that you mix some proportion of real robot data with other data sources, like simulation or egocentric human video data. This is especially important because robotics data tends to lack diversity which can be somewhat compensated for by the inclusion of these other modalities. And yet there has not been a sizable study on what constitute good practices for cotraining until now! We talk to Fanqi Lin and Jose Barreiros about their new work, a massive study which evaluated 89 policies over thousands of rollouts to tell us which forms of co-training were most useful for robotics. Watch episode 70 of RoboPapers, with Michael Cho and Chris Paxton, now!

Co-training has become a key part of the recipe for training large robotics models; it means that you mix some proportion of real robot data with other data sources, like simulation or egocentric human video data. This is especially important because robotics data tends to lack diversity which can be somewhat compensated for by the inclusion of these other modalities. And yet there has not been a sizable study on what constitute good practices for cotraining until now! We talk to Fanqi Lin and Jose Barreiros about their new work, a massive study which evaluated 89 policies over thousands of rollouts to tell us which forms of co-training were most useful for robotics. Watch episode 70 of RoboPapers, with Michael Cho and Chris Paxton, now!

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🚨Is it possible to devise an intuitive approach for crowdsourcing trainable data for robots without requiring a physical robot🤖? Can we democratize robot learning for all?🧑‍🤝‍🧑 Check out our latest #CoRL2023 paper-> AR2-D2: Training a Robot Without a Robot

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Humanoid robot development is too fragmented to scale. 🤖 With NVIDIA Isaac GR00T, developers unify simulation, data, and training in one open platform, cutting setup time from days to hours. See how Isaac GR00T helps research teams move faster from robot bring-up to skill development and real-world validation. Watch now 📺

NVIDIA Robotics

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From Conference on Robot Learning, cofounder Michael Cho - Rbt/Acc shared BitRobot’s vision with the world’s leading robotics minds: Crypto will drastically accelerate embodied AI research. We're building an open network where anyone can contribute to robotics R&D and get rewarded. Watch the keynote ↓

From Conference on Robot Learning, cofounder Michael Cho - Rbt/Acc shared BitRobot’s vision with the world’s leading robotics minds: Crypto will drastically accelerate embodied AI research. We're building an open network where anyone can contribute to robotics R&D and get rewarded. Watch the keynote ↓

BitRobot Network

20,804 次观看 • 10 个月前

Gemini Robotics 2 by Google DeepMind uses sim-only post-training on a mixed-data base model to handle automotive kitting. Contact-rich tasks like these have long been considered one of the hardest challenges in Sim2Real. Gemini Robotics 2 shows simulation can work even here. General robot intelligence needs much more data than real-robot collection alone can provide. Simulation has proven to be an effective way to fill that gap.

Gemini Robotics 2 by Google DeepMind uses sim-only post-training on a mixed-data base model to handle automotive kitting. Contact-rich tasks like these have long been considered one of the hardest challenges in Sim2Real. Gemini Robotics 2 shows simulation can work even here. General robot intelligence needs much more data than real-robot collection alone can provide. Simulation has proven to be an effective way to fill that gap.

Axis Robotics

14,454 次观看 • 14 天前

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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

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JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,373 次观看 • 15 天前

NVIDIA open models provide the foundation to innovate across agentic AI, robotics, autonomous vehicles, and research. 🤖 This release contributes 10 trillion language tokens, 500,000 robotics trajectories, and 100 terabytes of vehicle sensor data to the community. These frameworks are designed to significantly accelerate specialized AI development and discovery. Learn more:

NVIDIA open models provide the foundation to innovate across agentic AI, robotics, autonomous vehicles, and research. 🤖 This release contributes 10 trillion language tokens, 500,000 robotics trajectories, and 100 terabytes of vehicle sensor data to the community. These frameworks are designed to significantly accelerate specialized AI development and discovery. Learn more:

NVIDIA

36,408 次观看 • 5 个月前

Everyone is scaling VLAs with more robot data. TiPToP shows another path. No robot training, no policy learning. Just RGB + language → 3D scene → GPU TAMP planner → trajectory. Foundation models + planning alone can run real manipulation tasks.

Everyone is scaling VLAs with more robot data. TiPToP shows another path. No robot training, no policy learning. Just RGB + language → 3D scene → GPU TAMP planner → trajectory. Foundation models + planning alone can run real manipulation tasks.

Robots Digest 🤖

10,362 次观看 • 5 个月前

No wonder they say teleoperation is dead Peking University’s DAGroup released HumanNet, a massive 1 million hour dataset of human-centric videos that turns everyday internet footage into gold for embodied AI. It’s got first-person and third-person views, super detailed actions, object interactions, tool use, and long sequences of real behavior. Everything’s cleaned up and annotated with 3D poses, SLAM tracks, and robot-ready labels. The crazy part is,Training on just 1,000 hours of their first-person videos performs as well as (or even slightly better than) 100 hours of actual robot teleop data on downstream tasks. Basically, human videos can now stand in for expensive robot data. Scaling laws for robotics just got a whole lot more realistic. Human priors + smart curation = the future of world models.

No wonder they say teleoperation is dead Peking University’s DAGroup released HumanNet, a massive 1 million hour dataset of human-centric videos that turns everyday internet footage into gold for embodied AI. It’s got first-person and third-person views, super detailed actions, object interactions, tool use, and long sequences of real behavior. Everything’s cleaned up and annotated with 3D poses, SLAM tracks, and robot-ready labels. The crazy part is,Training on just 1,000 hours of their first-person videos performs as well as (or even slightly better than) 100 hours of actual robot teleop data on downstream tasks. Basically, human videos can now stand in for expensive robot data. Scaling laws for robotics just got a whole lot more realistic. Human priors + smart curation = the future of world models.

CyberRobo

27,048 次观看 • 3 个月前