通过加入接触与受力信息，提升四足机器人线性策略的稳定性和适应性。
Ground contact and reaction force sensing for linear policy control of quadruped robot
- 在策略观察空间中加入地面接触和反作用力数据
- 策略在未训练环境中生存率更高，抗干扰能力更强
- 适合需要轻量控制的复杂地形机器人应用
设计能穿越不平整地形并克服物理障碍的机器人一直是机器人领域的长期挑战。步行机器人凭借其灵活性、冗余自由度以及运动附肢的间歇性地面接触展现出潜力。然而，步行机器人的复杂性和众多自由度使其控制极为困难且计算量大。采用强化学习训练的线性策略已被证明可有效实现四足行走，同时计算开销小。本研究旨在探究在线性策略的观察空间中引入新状态变量对其性能的影响。由于地面接触和反作用力是机器人与环境交互的主要方式，这些变量对策略至关重要。实验结果表明，将地面接触和反作用力数据加入观察空间后，训练出的策略具有更好的生存能力、更强的外部扰动抵抗能力，以及更高的未训练条件适应性。
原文摘要 · Abstract (English)
Designing robots capable of traversing uneven terrain and overcoming physical obstacles has been a longstanding challenge in the field of robotics. Walking robots show promise in this regard due to their agility, redundant DOFs and intermittent ground contact of locomoting appendages. However, the complexity of walking robots and their numerous DOFs make controlling them extremely difficult and computation heavy. Linear policies trained with reinforcement learning have been shown to perform adequately to enable quadrupedal walking, while being computationally light weight. The goal of this research is to study the effect of augmentation of observation space of a linear policy with newer state variables on performance of the policy. Since ground contact and reaction forces are the primary means of robot-environment interaction, they are essential state variables on which the linear policy must be informed. Experimental results show that augmenting the observation space with ground contact and reaction force data trains policies with better survivability, better stability against external disturbances and higher adaptability to untrained conditions.
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