CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation

[2606.27676] CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation

[Submitted on 26 Jun 2026]

Title:CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation

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Abstract:Achieving everyday tasks with humanoid robots requires coordinating stable locomotion with versatile manipulation. However, existing whole-body controllers still face significant challenges. Methods trained solely via command sampling, without motion-capture (MoCap) data, often struggle with sparse rewards and require carefully tuned curricula to converge. This is especially problematic for upper-body control, where the resulting motions deviate from human-like statistics and degrade whole-body coordination. Conversely, approaches that imitate full-body MoCap data suffer from dataset imbalance, as many locomotion trajectories are overly aggressive for stable-locomotion scenarios, necessitating extensive data filtering and augmentation. To address this, we present Composite Whole-Body Imitation (CWI), a framework that decouples the use of MoCap data for upper-body manipulation and lower-body locomotion. This decoupling allows us to exploit the full MoCap dataset of diverse manipulation references, while stable, command-conditioned lower-body locomotion is guided by dual discriminators trained on curated expert-quality walking and squatting clips via an Adversarial Motion Prior (AMP). A multi-critic architecture reduces conflicts among locomotion, manipulation, and motion-style objectives, and a teacher--student distillation stage yields a whole-body policy conditioned only on bimanual hand poses and velocity/height commands. We evaluate CWI through simulation experiments and real-world deployment on a full-size LimX Oli humanoid. The results show competitive loco-manipulation performance, robust whole-body coordination, and practical teleoperation without full-body motion-capture equipment. A project page with supplementary material can be found at this https URL.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2606.27676 [cs.RO]

(or arXiv:2606.27676v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.27676

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Wenqi Ge [view email] [v1] Fri, 26 Jun 2026 03:14:52 UTC (5,187 KB)

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