Retargeting human motion to humanoids requires preserving motion semantics while accounting for the robot's physical constraints. Kinematic pipelines prescribe a robot trajectory through geometric matching, without establishing whether it can be executed under the robot's dynamics. Prior physics-supervised neural metho...
Chun Gu, Jun-Tong Zhang, Jing-Yu Li et al.· 0 citations
Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language models (VLMs) and video generative models (VGMs), respectively. Yet VLAs learn without explicit supervision from future world states, while WAMs that condition actions on pr...
Zi-Pei Ma, Jun-Zhe Jiang, Jia-Hui Zhang et al.· 0 citations
Scaling humanoid whole-body control toward general-purpose deployment requires large human motion corpora and training experience shared across robot bodies. Existing methods usually train one policy per robot, leaving motion experience isolated across embodiments. We introduce X-WBC, a cross-embodiment foundation fram...
Jun-Tong Zhang, Chun Gu, Li Zhang· 0 citations
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