通过结合触觉引导和误差增强来改善基于奖励的强化后运动技能的保留

Dylan P. Losey, Laura H. Blumenschein, M. O'Malley
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引用次数: 5

摘要

已经有大量的研究旨在利用可编程机器人设备为人类用户提供触觉辅助或增强,以便新的运动技能可以有效地训练并在训练结束后长期保留。这些方法的成功程度各不相同,在训练期间接触这些控制器的小组通常不会更好地保留技能。这些发现表明,在设计与受训者的触觉互动时,需要对人类运动学习原理有更全面的了解。基于奖励的强化在提高技能保留方面的作用已被研究。根据任务难度、受试者能力和任务类型的不同,帮助用户完成任务的触觉引导和放大误差以增强对用户反馈的误差增强已被证明对训练是有益的。在本文中,我们将基于奖励的强化与这些机器人控制器的呈现相结合,以评估它们对视觉旋转任务中运动技能保留的影响,该任务具有可调难度,使用固定或移动目标。我们发现,在基于奖励的反馈模式下,在简单任务中,触觉引导和误差增强都能更好地保持期望的视觉运动偏移量,而在更复杂的任务中,只有接受触觉引导训练的受试者表现出优于未接受控制器训练的受试者的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the retention of motor skills after reward-based reinforcement by incorporating haptic guidance and error augmentation
There has been significant research aimed at leveraging programmable robotic devices to provide haptic assistance or augmentation to a human user so that new motor skills can be trained efficiently and retained long after training has concluded. The success of these approaches has been varied, and retention of skill is typically not significantly better for groups exposed to these controllers during training. These findings point to a need to incorporate a more complete understanding of human motor learning principles when designing haptic interactions with the trainee. Reward-based reinforcement has been studied for its role in improving retention of skills. Haptic guidance, which assists a user to complete a task, and error augmentation, which exaggerates error in order to enhance feedback to the user, have been shown to be beneficial for training depending on the task difficulty, subject ability, and task type. In this paper, we combine the presentation of reward-based reinforcement with these robotic controllers to evaluate their impact on retention of motor skill in a visual rotation task with tunable difficulty using either fixed or moving targets. We found that with the reward-based feedback paradigm, both haptic guidance and error augmentation led to better retention of the desired visuomotor offset during a simple task, while during a more complex task, only subjects trained with haptic guidance demonstrated performance superior to those trained without a controller.
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