机器人系统在行走和伸手时的振荡潜力动力学
Oiwi Parker Jones1, Alexander L Mitchell2,3, Jun Yamada4
1Applied AI Lab, Oxford Robotics Institute, University of Oxford, Oxford, UK. oiwi@robots.ox.ac.uk.
Scientific reports
|May 19, 2024
概括
研究人员演示了使用振荡动力学来控制机器人在学习潜伏空间中的伸展运动. 这种方法推断和控制真正的机器人状态,显示可解释的工作空间运动,扩展先前的机器人运动工作.
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
- 计算神经科学是一种神经科学.
背景情况:
- 复杂机器人的感觉运动控制具有挑战性,传统方法依赖于层次优化或黑子政策.
- 神经科学表明,灵长类动物运动皮层中的极限循环与移动和伸手等复杂行为相关.
- 之前的研究表明,在已学习的潜空间中,振荡动力学可以控制机器人运动,但其适用于像达到这样的非周期性任务是不清楚的.
研究的目的:
- 为了研究振荡动力学控制的延伸到机器人达到任务,一个不那么明显的循环行为比移动.
- 通过使用振荡动力学来演示真实机器人状态的推断和控制在学习的表示中.
- 分析在达到过程中被学习的潜伏表示中编码的运动的可解释性.
主要方法:
- 开发了一种控制策略,在学习的潜空间中采用振荡动力学,用于机器人到达.
- 将该方法应用于物理机器人平台,超越计算模拟.
- 在学习的表示中推断和控制机器人状态,分析结果的动态.
主要成果:
- 成功演示了在完成任务时使用振荡动态的真实机器人状态的推断和控制.
- 展示了学习的潜伏表示在机器人的工作空间内编码可解释的运动.
- 观察到到达动态,虽然不是完全循环的,但表现出由底层振荡力学驱动的循环模式.
结论:
- 振荡动力学可以有效地应用于控制机器人到达,超越循环移动任务.
- 学习的潜在表示与振荡控制相结合,为复杂的机器人行为提供了一个有希望的方法.
- 这些发现表明神经振荡机制与机器人控制动态任务之间的潜在联系.
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