{"ID":2828655,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.14689","arxiv_id":"2512.14689","title":"CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation","abstract":"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.","short_abstract":"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 hIsig...","url_abs":"https://arxiv.org/abs/2512.14689","url_pdf":"https://arxiv.org/pdf/2512.14689v2","authors":"[\"Sirui Chen\",\"Zi-ang Cao\",\"Zhengyi Luo\",\"Fernando Castañeda\",\"Chenran Li\",\"Tingwu Wang\",\"Ye Yuan\",\"Linxi \\\"Jim\\\" Fan\",\"C. Karen Liu\",\"Yuke Zhu\"]","published":"2025-12-16T18:56:04Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\"]","methods":"[]","has_code":false}