受执行器约束的强化学习用于高速四足机器人运动
Actuator-Constrained Reinforcement Learning for High-Speed Quadrupedal Locomotion
AI总结：
本文提出在执行器转矩-转速约束下进行强化学习，实现四足机器人高速奔跑，通过均匀分配转矩和轻量化足部设计，使45公斤的KAIST Hound达到6.5米/秒的最快速度。
摘要：
本文提出了一种在强化学习中考虑执行器转矩-转速工作区域来实现四足机器人高速奔跑的方法。训练过程中包含了执行器的物理特性和约束，以减少因电机转矩-转速限制而在现实世界中不可行的状态转换。步态奖励被设计为将电机转矩均匀分配到所有腿部，有助于更平衡的功率使用，并缓解由单电机饱和导致的性能瓶颈。此外，我们设计了一个轻量化足部以增强机器人的敏捷性。我们观察到，将电机工作区域作为约束应用有助于策略网络在采样过程中避免不可行区域。利用训练好的策略，KAIST Hound（一款45公斤的四足机器人）能够跑出高达6.5米/秒的速度，这是基于电机驱动的四足机器人中最快的速度。
英文摘要：
This paper presents a method for achieving high-speed running of a quadruped robot by considering the actuator torque-speed operating region in reinforcement learning. The physical properties and constraints of the actuator are included in the training process to reduce state transitions that are infeasible in the real world due to motor torque-speed limitations. The gait reward is designed to distribute motor torque evenly across all legs, contributing to more balanced power usage and mitigating performance bottlenecks due to single-motor saturation. Additionally, we designed a lightweight foot to enhance the robot's agility. We observed that applying the motor operating region as a constraint helps the policy network avoid infeasible areas during sampling. With the trained policy, KAIST Hound, a 45 kg quadruped robot, can run up to 6.5 m/s, which is the fastest speed among electric motor-based quadruped robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
