CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation

TL;DR

This paper introduces CHIP, a versatile module for humanoid robots that enables adaptive compliance control, allowing for diverse forceful manipulation tasks while maintaining agile motion tracking without complex tuning.

Contribution

CHIP is a novel plug-and-play module that provides controllable end-effector stiffness, enabling humanoids to perform various forceful tasks with a generalist controller.

Findings

Enables humanoids to perform diverse forceful manipulation tasks.

Maintains agile motion tracking during compliance adjustments.

Requires no data augmentation or reward tuning.

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.