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📢📢📢 Excited to release ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR25). 🤏🤙✌️With ManipTrans, we can transfer dexterous manipulation skills into robotic hands in simulation and deploy them on a real robot, using a residual policy learned for dex manipulation. 🤖🤖🤖The video below illustrates how the MoCap...

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Siyuan Huang

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20,918 görüntüleme • 1 yıl önce •via X (Twitter)

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Michael Black profil fotoğrafı
Michael Black1 yıl önce

Really exciting. Congratulations. See you at CVPR.

Siyuan Huang profil fotoğrafı
Siyuan Huang1 yıl önce

Thank you Michael, see you!

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Benzer Videolar

ManipTrans is out on Hugging Face Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning
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ManipTrans is out on Hugging Face Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning

AK

22,136 görüntüleme • 1 yıl önce

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)
1:15

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)

Zhenyu Jiang

44,263 görüntüleme • 1 yıl önce

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.
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How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.

Mandi Zhao

120,304 görüntüleme • 1 yıl önce

Sim2Real RL for Vision-Based Dexterous Manipulation on Humanoids TLDR - we train a humanoid robot with two multifingered hands to perform a range of dexterous manipulation tasks robust generalization and high performance without human demonstration :D
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Sim2Real RL for Vision-Based Dexterous Manipulation on Humanoids TLDR - we train a humanoid robot with two multifingered hands to perform a range of dexterous manipulation tasks robust generalization and high performance without human demonstration :D

Toru

49,561 görüntüleme • 1 yıl önce

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇
0:55

Tactile feedback is one of the most important modalities in manipulation, but has been underutilized in dexterous hands. T-Dex is a framework for learning dexterous policies from tactile play data, beating vision and torque-based methods by 1.7x. 🧵👇

Lerrel Pinto

84,258 görüntüleme • 3 yıl önce

Advancing dexterous manipulation through scalable visual sim-to-real transfer. We are excited to share our RSS paper, “ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation.” 🌐 Project page: 1/N 🧵
0:30

Advancing dexterous manipulation through scalable visual sim-to-real transfer. We are excited to share our RSS paper, “ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation.” 🌐 Project page: 1/N 🧵

Robotic Systems Lab

39,143 görüntüleme • 1 ay önce

For large-scale robotic deployment🤖 in the real-world 🌏, robots must adapt to changes in environment and objects. Ever questioned the generalizability of your robot's manipulation policy? Put it to the test with The Colosseum 🏛️. Check out our project:
1:01

For large-scale robotic deployment🤖 in the real-world 🌏, robots must adapt to changes in environment and objects. Ever questioned the generalizability of your robot's manipulation policy? Put it to the test with The Colosseum 🏛️. Check out our project:

Jiafei Duan

36,617 görüntüleme • 2 yıl önce

🤖 How can robot policies zero-shot generalize to any new environment and any new object? Introducing our new project: 🚀Data Scaling Laws in Imitation Learning for Robotic Manipulation🚀—bringing us closer to the dream of having robots work as waiters in hot pot restaurants! 🍲
0:29

🤖 How can robot policies zero-shot generalize to any new environment and any new object? Introducing our new project: 🚀Data Scaling Laws in Imitation Learning for Robotic Manipulation🚀—bringing us closer to the dream of having robots work as waiters in hot pot restaurants! 🍲

Yang Gao

124,995 görüntüleme • 1 yıl önce

Can we build a generalist robotic policy that doesn’t just memorize training data and regurgitate it during test time, but instead remembers past actions as memory and conditions its decisions on them?🤖💡 Introducing SAM2Act—a multi-view robotic transformer-based policy that integrates a visual foundation model with a memory architecture for robotic manipulation. Project page: 🧵👇
1:28

Can we build a generalist robotic policy that doesn’t just memorize training data and regurgitate it during test time, but instead remembers past actions as memory and conditions its decisions on them?🤖💡 Introducing SAM2Act—a multi-view robotic transformer-based policy that integrates a visual foundation model with a memory architecture for robotic manipulation. Project page: 🧵👇

Jiafei Duan

87,573 görüntüleme • 1 yıl önce

A big part of scaling robot learning to solve real-world problems is that we somehow need to get enough diverse, high-quality data to train our robots to perform useful things. GPT and its fellow large language models were bootstrapped and proved out on a massive dataset of real-world language data. Unfortunately, despite our best efforts, similarly massive datasets don’t really exist for robotics — so, in our unending pursuit of high-quality, useful data, we turn to simulation. I compared a couple recent works on sim-to-real robot manipulation, which discuss how to train perception-driven manipulation policies in simulation, in such a way that they’re useful in the real world. - DextraH-RGB, from NVIDIA - Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, also from NVIDIA — specifically the GEAR lab - Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids, another GEAR lab paper - Local Policies Enable Zero-shot Long-Horizon Manipulation, from CMU (video from DextrAH-RGB)
0:33

A big part of scaling robot learning to solve real-world problems is that we somehow need to get enough diverse, high-quality data to train our robots to perform useful things. GPT and its fellow large language models were bootstrapped and proved out on a massive dataset of real-world language data. Unfortunately, despite our best efforts, similarly massive datasets don’t really exist for robotics — so, in our unending pursuit of high-quality, useful data, we turn to simulation. I compared a couple recent works on sim-to-real robot manipulation, which discuss how to train perception-driven manipulation policies in simulation, in such a way that they’re useful in the real world. - DextraH-RGB, from NVIDIA - Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation, also from NVIDIA — specifically the GEAR lab - Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids, another GEAR lab paper - Local Policies Enable Zero-shot Long-Horizon Manipulation, from CMU (video from DextrAH-RGB)

Chris Paxton

20,486 görüntüleme • 1 yıl önce

This dexterous robotic hand can see, feel, and solve a Rubik’s cube BrainCo's Revo 3 dexterous robotic hand, a Chinese-developed system with integrated vision, full-palm tactile sensors, and high-precision actuators for complex manipulation tasks
0:37

This dexterous robotic hand can see, feel, and solve a Rubik’s cube BrainCo's Revo 3 dexterous robotic hand, a Chinese-developed system with integrated vision, full-palm tactile sensors, and high-precision actuators for complex manipulation tasks

Science girl

19,923 görüntüleme • 2 ay önce

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)
0:41

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

Abhishek Gupta

13,345 görüntüleme • 1 yıl önce

🤖 What if a humanoid robot could make a hamburger from raw ingredients—all the way to your plate? 🔥 Excited to announce ViTacFormer: our new pipeline for next-level dexterous manipulation with active vision + high-resolution touch. 🎯 For the first time ever, we demonstrate ~2.5 minutes of continuous, autonomous control—combining active vision, high-res touch, and high-DoF robot hands SharpaWave — to complete complex, real-world tasks. Code is fully released; check out our: Homepage: Paper link: Github:
3:55

🤖 What if a humanoid robot could make a hamburger from raw ingredients—all the way to your plate? 🔥 Excited to announce ViTacFormer: our new pipeline for next-level dexterous manipulation with active vision + high-resolution touch. 🎯 For the first time ever, we demonstrate ~2.5 minutes of continuous, autonomous control—combining active vision, high-res touch, and high-DoF robot hands SharpaWave — to complete complex, real-world tasks. Code is fully released; check out our: Homepage: Paper link: Github:

Haoran Geng

95,044 görüntüleme • 11 ay önce

Cross-embodied robot policies hold the promise of one policy to control all robots. But how far does transfer go? In new work, we study positive transfer between *manipulation* & *navigation* and show that nav data helps manipulation, and vice versa! 🧵 👇
0:53

Cross-embodied robot policies hold the promise of one policy to control all robots. But how far does transfer go? In new work, we study positive transfer between *manipulation* & *navigation* and show that nav data helps manipulation, and vice versa! 🧵 👇

Sergey Levine

42,712 görüntüleme • 2 yıl önce

Tesla Optimus can arrange batteries in their factories, ours can do skincare (on Yuzhe Qin)! We opensource Bunny-VisionPro, a teleoperation system for bimanual hand manipulation. The users can control the robot hands in real time using VisionPro, flexible like a bunny. 🐇 We also have kitchen tasks, playing Rubik's Cube, and dynamic motion tasks. Imitation learning policies are trained on sweeping with a broom, serving a drink, and wiping glasses. Check our website for more details: The project is led by Runyu Ding Runyu Ding, Yuzhe Qin Yuzhe Qin , and Jiyue Zhu
2:56

Tesla Optimus can arrange batteries in their factories, ours can do skincare (on Yuzhe Qin)! We opensource Bunny-VisionPro, a teleoperation system for bimanual hand manipulation. The users can control the robot hands in real time using VisionPro, flexible like a bunny. 🐇 We also have kitchen tasks, playing Rubik's Cube, and dynamic motion tasks. Imitation learning policies are trained on sweeping with a broom, serving a drink, and wiping glasses. Check our website for more details: The project is led by Runyu Ding Runyu Ding, Yuzhe Qin Yuzhe Qin , and Jiyue Zhu

Xiaolong Wang

90,755 görüntüleme • 2 yıl önce

AGIBOT introduces AgiBot World, a large-scale open-source dataset for general-purpose robotic learning. ⦿ 1M+ trajectories from 100+ real-world scenarios ⦿ Scenarios include dexterous manipulation, tool use, and multi-robot collaboration
1:16

AGIBOT introduces AgiBot World, a large-scale open-source dataset for general-purpose robotic learning. ⦿ 1M+ trajectories from 100+ real-world scenarios ⦿ Scenarios include dexterous manipulation, tool use, and multi-robot collaboration

The Humanoid Hub

59,847 görüntüleme • 1 yıl önce

I have Post-Doc, PhD, and Research Assistant positions available, for a new project working with me on dexterous robot learning! Come and join me, my team, and our robots, at Imperial College London! 🤖🧑‍💻🇬🇧💂🤖 See for more info. Please share/retweet!
0:32

I have Post-Doc, PhD, and Research Assistant positions available, for a new project working with me on dexterous robot learning! Come and join me, my team, and our robots, at Imperial College London! 🤖🧑‍💻🇬🇧💂🤖 See for more info. Please share/retweet!

Edward Johns

25,049 görüntüleme • 1 yıl önce

This is going to be the year of hands and dexterous manipulation :) A sneak-peak into what's been cooking in our lab:
1:08

This is going to be the year of hands and dexterous manipulation :) A sneak-peak into what's been cooking in our lab:

Pulkit Agrawal

22,883 görüntüleme • 2 yıl önce

Robot foundation models are limited by costly real data, while simulation data is plentiful but visually mismatched to reality. We present Point Bridge, a method that enables zero-shot sim-to-real transfer for robot learning with minimal visual alignment.
0:50

Robot foundation models are limited by costly real data, while simulation data is plentiful but visually mismatched to reality. We present Point Bridge, a method that enables zero-shot sim-to-real transfer for robot learning with minimal visual alignment.

Siddhant Haldar

19,818 görüntüleme • 4 ay önce