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Boston Dynamics’ robot-behavior team lead highlights three core initiatives aimed at advancing Atlas’s dexterity: ⦿ Reinforcement learning in simulation ⦿ Whole-body teleoperation to collect data for imitation learning ⦿ Tactile sensing grippers

The Humanoid Hub

60,420 subscribers

18,029 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治教育

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7 条评论

The Humanoid Hub 的头像

The Humanoid Hub1 年前

Source:

Rainmaker 的头像

Rainmaker2 年前

💡 Learn how Reinforcement Learning can boost your trading performance! In this free Substack article I share full code of a trading algorithm based on Reinforcement Learning that beats other Machine Learning models as well as simply buying and holding the stock.

Phil Trubey 的头像

Phil Trubey1 年前

The biggest thing from vid was that automatic retry when initial attempt fails is an emergent behavior that comes with sim training at scale. Very very interesting. Bolsters Tesla approach building out their huge compute clusters and manufacturing a fleet for teleop training.

Judge DredD 的头像

Judge DredD1 年前

Bad optimus copy

The Robot Services Exchange 的头像

The Robot Services Exchange1 年前

solid

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VentureMind AI1 年前

Working together to build the future 🤝

MatRat21 的头像

MatRat211 年前

Hell yeah Atlas!

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Reinforcement learning is used to speed the production of behavior for the Boston Dynamics Atlas humanoid robot. At the heart of the learning process is a physics-based simulator that generates training data for a variety of maneuvers.

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My last PhD project at CMU will be presented today and tomorrow at Conference on Robot Learning as one of the Demos: SIAT (SIAT: Stretch control with Immersive AR Teleoperation) Find Vidhi Jain, Su Li and Yonatan Bisk who will be presenting this work in person. The original goal of me doing this project is to collect realistic human-robot interaction demonstration data just like the way you interact with your friends. But you can also use this package for non-social task data collection as well.

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Training a Whole-Body Control Foundation Model -- new work from my team at Agility Robotics A neural network for controlling our humanoid robots which is robust to disturbances, can handle heavy objects, and is a powerful platform for learning new whole-body skills learn more in our blog post ->

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VLAI Robotics Launches High-Dexterity Dual-Arm Robot Starting at ~$5,500 USD VLAI Robotics, utilizing the open-source design of Japan’s OpenArm team, has introduced a high-dexterity, low-cost bimanual robotic system. The product is priced from 39,900 RMB (approximately 5,500 USD), making high-level research tools accessible to institutions and developers. The robot features a human-scale, arm-hand integrated design with up to 16 DOF (8 DOF per arm, including the gripper). It can handle a dual-arm peak payload of 12 kg while maintaining high precision. A key breakthrough is its ability to replicate human upper limb movement trajectories, which enhances data quality for imitation learning and remote operation. VLAI Robotics handled the domestic engineering, manufacturing, and VLA (Vision-Language-Action) algorithm integration, with quality control organized according to the strict standards of the OpenArm R&D framework. The robot is highly adaptable for research, education, and Physical AI training, supporting easy expansion for features like VR teleoperation.

RoboHub🤖

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Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

RoboPapers

30,004 次观看 • 2 个月前

I was really impressed by the UMI gripper (Cheng Chi et al.), but a key limitation is that **force-related data wasn’t captured**: humans feel haptic feedback through the mechanical springs, but the robot couldn’t leverage that info, limiting the data’s value for fine-grained manipulation tasks. Led by my amazing students Yolanda Zhu and Binghao Huang, we designed a **portable visuo-tactile gripper** by integrating our dense, flexible tactile arrays with the UMI gripper to enable large-scale in-the-wild data collection. 🔗 We demonstrate **cross-modal representation learning** and **downstream policy learning** on tasks requiring in-hand state estimation (e.g., test tube reorientation) and fine-grained force sensing (e.g., pipette fluid transfer). Key takeaways: - Our flexible tactile arrays store the rich haptic information humans perceive as dense tactile signals. - Portability and robustness are key for in-the-wild data collection; our portable gripper is compact, lightweight, and durable. - Touch provides precise, robust measurements of in-hand object pose, invariant to lighting and viewpoint. - Cross-modal pretraining on large-scale in-the-wild data significantly improves policy robustness and sample efficiency (as shown many times before — and verified again here!). Also check out our previous investigations of dense, flexible tactile grids for understanding human-robot-environment interactions: - Dense tactile glove (Nature ’19): - 3D-ViTac (CoRL ’24):

I was really impressed by the UMI gripper (Cheng Chi et al.), but a key limitation is that force-related data wasn’t captured: humans feel haptic feedback through the mechanical springs, but the robot couldn’t leverage that info, limiting the data’s value for fine-grained manipulation tasks. Led by my amazing students Yolanda Zhu and Binghao Huang, we designed a portable visuo-tactile gripper by integrating our dense, flexible tactile arrays with the UMI gripper to enable large-scale in-the-wild data collection. 🔗 We demonstrate cross-modal representation learning and downstream policy learning on tasks requiring in-hand state estimation (e.g., test tube reorientation) and fine-grained force sensing (e.g., pipette fluid transfer). Key takeaways: - Our flexible tactile arrays store the rich haptic information humans perceive as dense tactile signals. - Portability and robustness are key for in-the-wild data collection; our portable gripper is compact, lightweight, and durable. - Touch provides precise, robust measurements of in-hand object pose, invariant to lighting and viewpoint. - Cross-modal pretraining on large-scale in-the-wild data significantly improves policy robustness and sample efficiency (as shown many times before — and verified again here!). Also check out our previous investigations of dense, flexible tactile grids for understanding human-robot-environment interactions: - Dense tactile glove (Nature ’19): - 3D-ViTac (CoRL ’24):

Yunzhu Li

13,188 次观看 • 1 年前