通过强化学习来控制有腿机器人的动态落恢复控制
Sicen Li1,2, Yiming Pang1,2, Panju Bai1,2
1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.
Biomimetics (Basel, Switzerland)
|April 26, 2024
概括
有腿的机器人现在可以使用新的深度强化学习框架从跌倒中恢复. 这种方法可以从干扰中进行动态恢复,提高机器人在现实环境中的弹性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 有腿的机器人在非结构化的环境中面临着不可避免的跌倒.
- 现有的方法在秋季恢复过程中与复杂的,未指定的接触作斗争.
- 动态恢复对于不间断的机器人运动至关重要.
研究的目的:
- 开发一个新的框架,用于动态倒闭恢复在腿类机器人.
- 应对外部干扰和未指定的接触所带来的挑战.
- 为了实现强大的机动运动,尽管落下事件.
主要方法:
- 引入了一个深度强化学习框架.
- 训练了一个基于学习的状态估计器.
- 为动态的秋季复苏制定了一个自身感知历史政策.
- 将框架应用于各种室内和室外落场景.
主要成果:
- 拟议的框架有效地训练机器人进行动态的摔倒恢复.
- 学习的政策证明了硬件可行性和现实世界的实施.
- 用四足机器人进行的广泛试验证实了其在平面和草地上的高效性.
结论:
- 新的深度强化学习框架显著提高了腿类机器人的动态摔倒恢复能力.
- 该方法为现实世界机器人应用提供了强大的解决方案.
- 这项研究为更有弹性和自主脚型机器人铺平了道路.
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