从生物狗中学习和重复使用四重机器人运动技能,用于更高级别的任务
Qifeng Wan1, Aocheng Luo1, Yan Meng1
1State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究介绍了四足机器人运动控制的等级强化学习框架,使灵活的生物仿真运动成为可能. 这种无模型的方法简化了开发,并提高了机器人在复杂地形中的适应性.
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:
- 模型预测控制 (MPC) 是用于四足机器人运动控制的经典但复杂的方法,需要精确的动态模型来限制灵活性.
- 无模型学习方法提供了一个有希望的替代方案,减少模拟困难和计算负载,以加强机器人控制.
- 生物系统为开发复杂的运动控制策略提供灵感.
研究的目的:
- 为四足机器人运动控制提出一个层次化的强化学习 (HRL) 框架.
- 以动物发育为灵感,使机器人能够通过建立基础运动技能来学习复杂的任务.
- 为了实现更灵活,仿生运动,并提高在充满挑战的环境中的适应能力.
主要方法:
- 提出了一个层次化的强化学习框架,灵感来自生物学习过程.
- 基本的运动技能是从生物狗运动数据中学习的.
- 在训练期间使用域随机化技术,以便将强大的政策转移到物理机器人.
- 更高级别的任务是使用先前学习的基本技能来学习的,减少了冗余的培训.
主要成果:
- 拟议的HRL框架有助于四足机器人学习更高层次的运动任务.
- 经过训练的政策表明,由于域随机化,可以直接转移到物理机器人.
- 控制器可以实现更多的仿生运动,增强机器人的敏捷性和适应性.
- 该方法可以通过利用机器人的能力在复杂的地形上进行高效的操作.
结论:
- 层次增强学习为先进的四足机器人运动控制提供了一种有效的无模型方法.
- 该框架减少了开发复杂性和培训时间,同时提高了性能.
- 该方法增强了仿生学,敏捷性和适应性,为复杂环境中的机器人铺平了道路.
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