Title: Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots Show Chinese title

Title: 腿足机器人混合力位运动策略中的扰动感知自适应补偿

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. Show Chinese abstract

Abstract: 基于强化学习（RL）的方法显著提升了腿足机器人行走性能。然而，当这些运动策略部署到现实世界时，面临着重大挑战。在不确定环境中运行的机器人难以适应载荷变化和外部干扰，导致运动性能严重退化。在这项工作中，我们提出了一种新颖的混合力位移步态策略（HFPLP）学习框架，该框架将策略的动作空间定义为目标关节位置与前馈扭矩的组合，使机器人能够快速响应载荷变化和外部干扰。此外，所提出的扰动感知自适应补偿（DAAC）在扭矩空间中根据外部干扰估计提供补偿动作，增强了机器人对动态环境变化的适应能力。我们在仿真和真实场景中验证了我们的方法，证明其在搬运负载和抵抗干扰方面优于现有方法。