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ExBody2: Advanced Expressive Humanoid Whole-Body Control

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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.

Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, Xiaolong Wang• 2024

Related benchmarks

TaskDatasetResultRank
Humanoid motion trackingMotionX (test)
Success Rate88
9
Motion TrackingNymeria (test)
Success Rate94
6
Humanoid ControlAMASS IsaacLab ID (test)
Success Rate86.6
5
Humanoid ControlAMASS IsaacLab + DR ID (test)
MPJPE (Egocentric, mm)237
5
Humanoid ControlAMASS Genesis OOD (test)
Ego MPJPE (mm)305
5
Humanoid ControlAMASS Genesis + DR OOD (test)
Eg-MPJPE (mm)342
5
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