学习灵活性和自适应性腿部运动通过课程后视力强化学习
Sicen Li1, Gang Wang2, Yiming Pang1
1The College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, China.
Scientific reports
|November 14, 2024
概括
我们开发了课程回顾强化学习 (CHRL) 用于敏捷的腿类机器人运动. 该系统使机器人能够执行复杂的机动作,例如在各种地形上恢复落和高速运行.
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
- 机器学习 机器学习
背景情况:
- 敏捷和适应性移动对于在非结构化环境中运行的腿类机器人至关重要.
- 现有的方法与动态机动作作斗争,如倒恢复,高速转和冲刺.
- 现实世界的部署需要强大的控制器,能够处理意想不到的干扰和各种地形.
研究的目的:
- 开发一个端到端的跟踪控制器,以提高腿类机器人的敏捷性和适应性.
- 为了实现自主摔倒恢复和高速移动能力.
- 为了创建一个系统,以响应不断变化的环境条件,展现适应性行为.
主要方法:
- 提出了一个新的课程回顾强化学习 (CHRL) 框架.
- 实施了自动课程策略,逐步增加任务难度.
- 适应后视体验重播 (HER) 对于在腿部运动任务中高效学习.
主要成果:
- 在一个真正的四足机器人上展示了成功的敏捷和适应性机动.
- 实现了自主摔倒恢复和连贯的行走步态.
- 持续的户外运行速度高达3.45m/s,最大的曲率为3.2rad/s.
- 在草地和泥土等自然地形上展示了适应性行为.
结论:
- CHRL有效地学习了强大的敏捷性和适应性,用于腿类机器人.
- 拟议的系统可以在动态和不可预测的环境中实现稳健的运动.
- 这种方法提升了腿类机器人在现实世界应用中的能力.
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