自适应中央模式生成器控制人/机器人交互

P. Hénaff
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引用次数: 0

摘要

该演讲将涉及基于控制生物系统的特定生物感觉运动回路功能的仿生机器人控制器的使用。这些循环基于特定的神经网络结构,称为中枢模式发生器(CPG),它隐含在适应性节奏运动的发生和学习中。因此,更好地理解和建模这些结构,使类人机器人能够学习有节奏的运动或与人类互动,是很有趣的。在简要介绍了生物中心模式发生器和有节奏的运动之后,我们将介绍同步的概念,这是人类和动态振荡器之间有节奏的相互作用的基础原理。本文将介绍基于动态振荡器的中心模式发生器的不同模型。报告的第二部分将介绍几个使用自适应中心模式生成器的基于视觉的人机运动协调实验。介绍机器人遥操作工业节奏任务的其他实验。几个模拟和实验的视频将说明演示。演讲将以结论和观点结束。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive central pattern generators to control human/robot interactions
The presentation will concern the use of bio-inspired robot controllers based on the functioning of specific biological sensorimotor loops that control biological systems. These loops are based on specific neural network structures, called central pattern generators (CPG) that are implied in the genesis and learning of adaptive rhythmic movements. Therefore, it is interesting to better understanding and modeling these structures to have humanoid robots able to learn rhythmic movements for locomotion or for interacting with humans. After a brief introduction on biological central pattern generators and the rhythmic movements, we will introduce the concept of synchronization a principle that underlies the rhythmic interaction between humans and the dynamic oscillators. Different models of central pattern generators based on dynamic oscillators will be introduced. The second part of the presentation will present several experiments of vision-based Human-Robot motor coordination using adaptive central pattern generators. Other experiments of robot teleoperation for industrial rhythmic tasks will be introduced. Several videos of simulations and experiments will illustrate the presentation. A conclusion and perspectives will conclude the talk.
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