Recent progress in humanoid robots has unlocked agile locomotion skills, including backflipping, running, and crawling. Yet it remains challenging for a humanoid robot to perform forceful manipulation tasks such as moving objects, wiping, and pushing a cart. We propose adaptive Compliance Humanoid control through hIsight Perturbation (CHIP), a plug-and-play module that enables controllable end-effector stiffness while preserving agile tracking of dynamic reference motions. CHIP is easy to implement and requires neither data augmentation nor additional reward tuning. We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks that require different end-effector compliance, such as multi-robot collaboration, wiping, box delivery, and door opening.
@article{arxiv.2512.14689,
title = {CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation},
author = {Sirui Chen and Zi-ang Cao and Zhengyi Luo and Fernando Castañeda and Chenran Li and Tingwu Wang and Ye Yuan and Linxi "Jim" Fan and C. Karen Liu and Yuke Zhu},
journal= {arXiv preprint arXiv:2512.14689},
year = {2026}
}
Comments
The first two authors contributed equally. Project page: https://nvlabs.github.io/CHIP/