通过深度强化学习来控制Solo12四足机器人
Michel Aractingi1,2, Pierre-Alexandre Léziart3, Thomas Flayols3
1LAAS-CNRS, Université de Toulouse, 31400, Toulouse, France. Michel.aractingi@gmail.com.
Scientific reports
|July 24, 2023
概括
研究人员为四足机器人开发了一种深度强化学习控制器,使其能够在各种地形上进行强大的运动. 这种高效,可部署的控制器增强了Solo12机器人.
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:
- 四足机器人需要复杂环境的先进机 locomotion.
- 目前的控制方法可能缺乏稳定性和通用性.
研究的目的:
- 为四足机器人实施和评估基于学习的控制器.
- 在Solo12平台上展示强大和节能的机动车辆.
主要方法:
- 利用深度强化学习 (DRL) 进行联合阻抗控制.
- 开发了一个端到端的学习框架,用于运动控制.
- 专注于将政策从模拟转移到真正的机器人.
主要成果:
- 实现了强大的速度跟踪和节能移动.
- 成功将学习的策略转移到真正的Solo12机器人.
- 在各种室内和室外地形上表现出有效的性能.
结论:
- 索洛12机器人是一个有效的开源平台,用于基于学习的控制研究.
- 端到端的DRL控制器为强大的四足运动提供了可行的路径.
- 拟议的方法有助于轻松部署和转移学习的机动技能.
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