让四足机器人自动调参，自适应不同地形并省电
GainAdaptor: Learning Quadrupedal Locomotion with Dual Actors for Adaptable and Energy-Efficient Walking on Various Terrains
- 用双智能体框架动态调整关节PD参数
- 在真实机器人上实现多地形更稳更省电的行走
- 适合做机器人自适应控制的科研与工程人员
深度强化学习（DRL）为复杂环境中四足机器人的控制提供了创新方案，采用简洁架构。传统控制方法如逆动力学或基于比例-微分（PD）的关节位置控制，在使用DRL时面临直接力矩控制困难的问题，因此常采用关节位置控制，但需手动调节PD增益，限制了适应性与效率。本文提出GainAdaptor，一种自适应增益控制框架，可自动调节关节PD增益以提升地形适应性和能效。该框架采用双智能体算法，根据地面条件动态调整增益，通过分拆动作空间实现稳定高效的运动学习。实验在Unitree Go1机器人上验证，展示了在多种地形下性能显著提升。
原文摘要 · Abstract (English)
Deep reinforcement learning (DRL) has emerged as an innovative solution for controlling legged robots in challenging environments using minimalist architectures. Traditional control methods for legged robots, such as inverse dynamics, either directly manage joint torques or use proportional-derivative (PD) controllers to regulate joint positions at a higher level. In case of DRL, direct torque control presents significant challenges, leading to a preference for joint position control. However, this approach necessitates careful adjustment of joint PD gains, which can limit both adaptability and efficiency. In this paper, we propose GainAdaptor, an adaptive gain control framework that autonomously tunes joint PD gains to enhance terrain adaptability and energy efficiency. The framework employs a dual-actor algorithm to dynamically adjust the PD gains based on varying ground conditions. By utilizing a divided action space, GainAdaptor efficiently learns stable and energy-efficient locomotion. We validate the effectiveness of the proposed method through experiments conducted on a Unitree Go1 robot, demonstrating improved locomotion performance across diverse terrains.
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