学习四足高速在不平坦的地形上跑步
1Department of Automation, Tsinghua University, Beijing 100084, China.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
本研究引入了一种新的四足机器人控制框架,将低频增强学习 (RL) 与高频基于模型的控制相结合. 这种方法提高了稳定性,并克服了在不平坦地形上高速移动的延迟问题.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 强化学习 (RL) 控制器用于四足机器人在不平坦的地形上移动.
- 高速运动会放大外部干扰,影响机器人的稳定性.
- 现有的RL方法需要高的控制频率,导致显著的计算成本.
研究的目的:
- 提出一种新的控制框架,可以降低计算成本,同时保持高速四足机器人运动的稳定性.
- 为解决动态机器人应用基于RL的控制系统中的延迟挑战.
主要方法:
- 一个混合控制框架,将基于低频RL的控制政策与基于高频模型的联合控制器相结合.
- 该RL政策输出联合控制法律,由高频控制器执行以减轻干扰.
- 在模拟地形上进行的评估,高度差异不同 (高达6厘米).
主要成果:
- 在模拟中实现了1.8米/秒的运行速度,Unitree A1在模拟中翻了四倍.
- 该RL政策运行在50Hz与20毫秒的延迟,而联合控制器运行在1000Hz.
- 证明有效缓解外部干扰和延迟问题.
结论:
- 拟议的框架成功克服了低频RL更新的延迟限制.
- 这种方法适用于现实世界部署高速四足机器人.
- 在具有挑战性的地形上实现稳定和高效的移动.
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