Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots

TL;DR

This paper introduces a hybrid locomotion policy with disturbance-aware adaptive compensation for legged robots, significantly improving their ability to handle payload variations and external disturbances in real-world environments.

Contribution

The paper presents a novel hybrid force-position policy and a disturbance-aware adaptive compensation framework, enhancing robustness and adaptability of legged robots during locomotion.

Findings

Outperforms existing methods in payload carrying tasks

Demonstrates effective disturbance resistance in real-world tests

Enhances adaptability to dynamic environmental changes

Abstract

Reinforcement Learning (RL)-based methods have significantly improved the locomotion performance of legged robots. However, these motion policies face significant challenges when deployed in the real world. Robots operating in uncertain environments struggle to adapt to payload variations and external disturbances, resulting in severe degradation of motion performance. In this work, we propose a novel Hybrid Force-Position Locomotion Policy (HFPLP) learning framework, where the action space of the policy is defined as a combination of target joint positions and feedforward torques, enabling the robot to rapidly respond to payload variations and external disturbances. In addition, the proposed Disturbance-Aware Adaptive Compensation (DAAC) provides compensation actions in the torque space based on external disturbance estimation, enhancing the robot's adaptability to dynamic environmental changes. We validate our approach in both simulation and real-world deployment, demonstrating that it outperforms existing methods in carrying payloads and resisting disturbances.