ExBody2: Advanced Expressive Humanoid Whole-Body Control

• URL: http://arxiv.org/abs/2412.13196v2
• Date: Wed, 12 Mar 2025 00:40:43 GMT
• Title: ExBody2: Advanced Expressive Humanoid Whole-Body Control
• Authors: Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, Xiaolong Wang,
• Abstract summary: We propose a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data.<n>We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics.<n>We observed significant improvement of tracking performance after fine-tuning on a small amount of data.
• Score: 16.69009772546575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We propose Advanced Expressive Whole-Body Control (Exbody2), a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data and then transferred to the real world. We introduce a technique for decoupling the velocity tracking of the entire body from tracking body landmarks. We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics and to automatically filter away infeasible whole-body motions. This two-step approach enabled us to produce a student policy that can be deployed on the robot that can walk, crouch, and dance. We also provide insight into the trade-off between versatility and the tracking performance on specific motions. We observed significant improvement of tracking performance after fine-tuning on a small amount of data, at the expense of the others.

Related papers

• DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References [18.947295547196774]
  We address the challenge of developing a generalizable neural tracking controller for dexterous manipulation from human references. We introduce an approach that curates large-scale successful robot tracking demonstrations. Our method achieves over a 10% improvement in success rates compared to leading baselines.
  arXiv  Detail & Related papers  (2025-02-13T18:59:13Z)
• Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots [13.229028132036321]
  Masked Humanoid Controller (MHC) supports standing, walking, and mimicry of whole and partial-body motions. MHC imitates partially masked motions from a library of behaviors spanning standing, walking, optimized reference trajectories, re-targeted video clips, and human motion capture data. We demonstrate sim-to-real transfer on the real-world Digit V3 humanoid robot.
  arXiv  Detail & Related papers  (2024-07-30T09:10:24Z)
• Agile and versatile bipedal robot tracking control through reinforcement learning [12.831810518025309]
  This paper proposes a versatile controller for bipedal robots. It achieves ankle and body trajectory tracking across a wide range of gaits using a single small-scale neural network. Highly flexible gait control can be achieved by combining minimal control units with high-level policy.
  arXiv  Detail & Related papers  (2024-04-12T05:25:03Z)
• Visual Whole-Body Control for Legged Loco-Manipulation [22.50054654508986]
  We study the problem of mobile manipulation using legged robots equipped with an arm. We propose a framework that can conduct the whole-body control autonomously with visual observations.
  arXiv  Detail & Related papers  (2024-03-25T17:26:08Z)
• Expressive Whole-Body Control for Humanoid Robots [20.132927075816742]
  We learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. With training in simulation and Sim2Real transfer, our policy can control a humanoid robot to walk in different styles, shake hands with humans, and even dance with a human in the real world.
  arXiv  Detail & Related papers  (2024-02-26T18:09:24Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• Real-World Humanoid Locomotion with Reinforcement Learning [92.85934954371099]
  We present a fully learning-based approach for real-world humanoid locomotion. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
  arXiv  Detail & Related papers  (2023-03-06T18:59:09Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars [80.05743236282564]
  Real-time tracking of human body motion is crucial for immersive experiences in AR/VR. We present a reinforcement learning framework that takes in sparse signals from an HMD and two controllers. We show that a single policy can be robust to diverse locomotion styles, different body sizes, and novel environments.
  arXiv  Detail & Related papers  (2022-09-20T00:25:54Z)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)
• Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis [32.22704734791378]
  Reinforcement learning has shown great promise for realistic human behaviors by learning humanoid control policies from motion capture data. It is still very challenging to reproduce sophisticated human skills like ballet dance, or to stably imitate long-term human behaviors with complex transitions. We propose a novel approach, residual force control (RFC), that augments a humanoid control policy by adding external residual forces into the action space.
  arXiv  Detail & Related papers  (2020-06-12T17:56:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.