Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instances where physical human–robot interaction has been significantly more effective than unassisted practice or human-mediated training. This article describes a hybrid shared control robot, which enhances task learning through kinesthetic feedback. The assistance assesses user actions using a task-specific evaluation criterion and selectively accepts or rejects them at each time instant. Through two human subject studies (total n = 68 ), we show that this hybrid approach of switching between full transparency and full rejection of user inputs leads to increased skill acquisition and short-term retention compared with unassisted practice. Moreover, we show that the shared control paradigm exhibits features previously shown to promote successful training. It avoids user passivity by only rejecting user actions and allowing failure at the task. It improves performance during assistance, providing meaningful task-specific feedback. It is sensitive to initial skill of the user and behaves as an “assist-as-needed” control scheme, adapting its engagement in real time based on the performance and needs of the user. Unlike other successful algorithms, it does not require explicit modulation of the level of impedance or error amplification during training and it is permissive to a range of strategies because of its evaluation criterion. We demonstrate that the proposed hybrid shared control paradigm with a task-based minimal intervention criterion significantly enhances task-specific training.

中文翻译：

---

基于任务的混合共享控制通过强制交互进行训练

尽管机器人平台可以在训练过程中为用户提供一致的练习和客观评估，但很少有实例表明人机交互比无辅助练习或人工干预训练更有效。本文介绍了一种混合共享控制机器人，它通过动觉反馈增强了任务学习。该协助使用特定于任务的评估标准来评估用户操作，并在每个时刻有选择地接受或拒绝它们。通过两项人类主题研究（总共 n = 68），我们表明，与无辅助练习相比，这种在完全透明和完全拒绝用户输入之间切换的混合方法可以提高技能获取和短期保留。而且，我们展示了共享控制范式展示了先前显示的促进成功训练的特征。它通过仅拒绝用户操作并允许任务失败来避免用户被动。它提高了援助期间的绩效，提供了有意义的特定于任务的反馈。它对用户的初始技能很敏感，并表现为“按需辅助”控制方案，根据用户的性能和需求实时调整其参与度。与其他成功的算法不同，它不需要在训练期间对阻抗或误差放大的水平进行显式调制，并且由于其评估标准，它允许一系列策略。我们证明所提出的具有基于任务的最小干预标准的混合共享控制范式显着增强了特定于任务的培训。