让机器人在扰动下仍能稳定行走，通过自适应补偿提升鲁棒性。
Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots
- 融合目标位姿与前馈力矩的混合控制策略
- 实测在负载变化和外部干扰下性能优于现有方法
- 适合需要高动态适应性的真实场景机器人
基于强化学习的腿式机器人运动策略虽显著提升了行走性能，但在真实环境中部署时仍面临挑战。面对不确定环境中的负载变化和外部扰动，现有策略易导致性能严重下降。本文提出一种新型混合力-位置运动策略（HFPLP），其动作空间由目标关节位姿与前馈力矩共同构成，使机器人能快速响应负载变化和外部扰动。此外，提出的扰动感知自适应补偿（DAAC）基于外部扰动估计，在力矩空间生成补偿动作，增强对动态环境变化的适应能力。我们在仿真和真实机器人上验证了该方法，结果表明其在携带负载和抵抗扰动方面均优于现有方法。
原文摘要 · Abstract (English)
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.
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