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Robot learning schemes that trade motion accuracy for command simplification

机译：权衡运动精度以简化命令的机器人学习方案

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This study was inspired by the human motor control system in its ability to accommodate a wide Varity of motions. By contrast, the biologically inspired robot learning controller usually encounters huge learning space problems in many practical applications. A hypothesis for the superiority of the human motor control system is that it may have simplified the motion command at the expense of motion accuracy. This tradeoff provides an insight into how fast and simple control can be achieved when a robot task does not demand high accuracy. Two motion command simplification schemes are proposed in this paper based on the equilibrium-point hypothesis for human motion control. investigation into the tradeoff between motion accuracy and command simplification reported in this paper was conducted using robot manipulators to generate signatures. Signature generation involves fast handwriting, and handwriting is a human skill acquired via practice. Because humans learn how to sign their names after they learn how to write, in the second learning process, they somehow learn to trade motion accuracy for motion speed and command simplicity, since signatures are simplified forms of original handwriting. Experiments are reported that demonstrate the effectiveness of the proposed schemes.
机译：这项研究的灵感来自于人类运动控制系统，它能够适应多种运动。相比之下，受生物启发的机器人学习控制器通常在许多实际应用中会遇到巨大的学习空间问题。人体电机控制系统优越性的一个假设是，它可能以运动精度为代价简化了运动命令。这种权衡使人深刻了解了当机器人任务不需要高精度时如何实现快速简单的控制。基于人体运动控制的平衡点假设，本文提出了两种运动指令简化方案。本文使用机器人操纵器对运动精度和命令简化之间的折衷进行了研究，以生成签名。签名生成涉及快速手写，而手写是通过实践获得的一种人类技能。由于人类在学会书写后便学会如何对自己的名字进行签名，因此在第二个学习过程中，由于签名是原始手写体的简化形式，因此他们以某种方式学会了以运动精度来换取运动速度和简化命令。据报道，实验证明了所提方案的有效性。

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