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

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Vai Viswanathan

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13,179 görüntüleme • 9 gün önce •via X (Twitter)

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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!
1:09:10

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

24,352 görüntüleme • 1 ay önce

.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!
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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!

Y Combinator

35,876 görüntüleme • 4 ay önce

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.
0:34

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.

PrismaX

149,395 görüntüleme • 11 gün önce

꧁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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꧁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.

The DATA Foundation

155,437 görüntüleme • 1 yıl önce

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
0:31

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

Jun Gao

103,490 görüntüleme • 24 gün önce

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 →
1:39

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 →

LeRobot

280,924 görüntüleme • 2 ay önce

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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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

NVIDIA Robotics

17,030 görüntüleme • 1 yıl önce

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!
1:24:52

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!

RoboPapers

19,152 görüntüleme • 3 ay önce

🔴 NVIDIA, "insansı robot" projesi GR00T'u duyurdu, dünyanın önde gelen robotik şirketlerini bir araya getiriyor. Projede yer alacak şirketler 👇🏻 🤖 Boston Dynamics 🤖 1X Technologies 🤖 Agility Robotics 🤖 Apptronik 🤖 Figure AI 🤖 Fourier Intelligence 🤖 Sanctuary AI 🤖 Unitree Robotics 🤖 XPENG Robotics
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🔴 NVIDIA, "insansı robot" projesi GR00T'u duyurdu, dünyanın önde gelen robotik şirketlerini bir araya getiriyor. Projede yer alacak şirketler 👇🏻 🤖 Boston Dynamics 🤖 1X Technologies 🤖 Agility Robotics 🤖 Apptronik 🤖 Figure AI 🤖 Fourier Intelligence 🤖 Sanctuary AI 🤖 Unitree Robotics 🤖 XPENG Robotics

Conflict

184,587 görüntüleme • 2 yıl önce

🚨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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🚨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

Jiafei Duan

38,871 görüntüleme • 2 yıl önce

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 📺
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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

21,711 görüntüleme • 8 gün önce

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 ↓
15:59

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 görüntüleme • 9 ay önce

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:
1:39

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

36,399 görüntüleme • 3 ay önce

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:
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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

42,804 görüntüleme • 5 ay önce

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.
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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 görüntüleme • 3 ay önce

Text-to-image generative models, meet robotics! We present ROSIE: Scaling RObot Learning with Semantically Imagined Experience, where we augment real robotics data with semantically imagined scenarios for downstream manipulation learning. Website: 🧵👇
0:26

Text-to-image generative models, meet robotics! We present ROSIE: Scaling RObot Learning with Semantically Imagined Experience, where we augment real robotics data with semantically imagined scenarios for downstream manipulation learning. Website: 🧵👇

Fei Xia

196,378 görüntüleme • 3 yıl önce

NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:
0:20

NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:

NVIDIA Robotics

165,502 görüntüleme • 1 ay önce

Pi0 from Physical Intelligence is one of the best generalist Vision-Language-Action (VLA) models for robotics (and predecessor of Pi0.5). 🧵 Here's how it works + how you can fine-tune it on your own robot for a simple task:
1:12

Pi0 from Physical Intelligence is one of the best generalist Vision-Language-Action (VLA) models for robotics (and predecessor of Pi0.5). 🧵 Here's how it works + how you can fine-tune it on your own robot for a simple task:

Ilia

32,956 görüntüleme • 9 ay önce

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.
0:52

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 görüntüleme • 1 ay önce