CEER2: Directional and Tunable End-Effector and Root Compliance for Humanoid Loco-Manipulation

2026-09-29

AI Summary

This study addresses the challenge of balancing external force responsiveness with motion accuracy and the lack of directional compliance control during physical interactions in humanoid robots. To this end, we propose a hierarchical reinforcement learning framework that integrates end-effector directionally adjustable compliance with a root-selective compliance mechanism. A high-level policy modulates a low-level whole-body tracking controller, leveraging historical proprioceptive information to achieve online contact force estimation and dynamic adjustment of compliance parameters. Both simulation and real-world experiments validate the effectiveness of the proposed framework across complex mobile manipulation tasks, including directional stiffness control, disturbance-rejection tracking, and collaborative object transportation.

Abstract

Humanoids are increasingly capable of tracking complex whole-body motions, but physical interaction introduces a different challenge. When a robot makes contact with a person or the environment, it needs to respond to external forces while preserving the motion needed for the task. This response can vary across directions in the end-effectors and on the body. For example, an end effector may need to accommodate contact force in one direction while maintaining motion accuracy in another, while the robot body may resist an external force or move with it. We present a compliance framework for humanoid loco-manipulation that combines directional and tunable end-effector (EE) compliance with selectable root compliance for external force rejection or force following. A hierarchical reinforcement learning controller modulates a fixed whole-body tracking policy through high-level EE and root commands, while interaction forces are estimated from proprioceptive history. Our simulation and real-world experiments on a humanoid demonstrate directional stiffness control, online stiffness adjustment, distinct root compliance, compliant manipulation, and collaborative carrying.

Problem

• humanoid loco-manipulation
• directional compliance
• end-effector compliance
• root compliance
• physical interaction

Innovation

• Humanoid Loco-Manipulation
• Directional Compliance
• Hierarchical Reinforcement Learning
• Stiffness Control
• Proprioceptive Force Estimation