Increasing the autonomy level of a robot hand to accomplish remote object manipulation tasks faster and easier is a new and promising topic in teleoperation. Such semi-autonomous telemanipulation, however, is very challenging due to the physical discrepancy between the human hand and the robot hand, along with the fine motion constraints required for the manipulation task. To overcome these challenges, the robot needs to learn how to assist the human operator in a preferred/intuitive way, which must provide effective assistance that the operator needs yet still accommodate human inputs, so the operator feels in control of the system (i.e., not counter-intuitive to the operator). Toward this goal, we develop novel data-driven approaches to stably learn what assistance is preferred from high data variance caused by the ambiguous nature of human operators. To avoid an extensive robot-specific training process, methods to transfer this assistance knowledge between different robot hands are discussed. Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper. Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts.

中文翻译：

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学习和转移半自主远程操作中首选辅助策略的知识

提高机器人手的自主水平以更快、更轻松地完成远程对象操作任务是远程操作中一个新的、有前途的课题。然而，由于人手和机器人手之间的物理差异，以及操作任务所需的精细运动约束，这种半自主远程操作非常具有挑战性。为了克服这些挑战，机器人需要学习如何以首选/直观的方式帮助人类操作员，这必须提供操作员需要的有效帮助，但仍能适应人类输入，因此操作员感觉在控制系统（即，不违反操作员的直觉）。为了这个目标，我们开发了新的数据驱动方法，以从人类操作员的模糊性质引起的高数据差异中稳定地学习哪些帮助是首选的。为了避免大量的机器人特定训练过程，讨论了在不同机器人手之间传输这种辅助知识的方法。通过远程控制三指抓手和两指抓手，进行了远程操作杯子的三个主要任务的实验：使用、移动和交接。结果表明，所提出的模型有效地学习了首选辅助的知识，并且机器人之间的知识转移允许这种半自主远程操作策略以较少的培训工作量来扩大规模。讨论了在不同机器人手之间传输这种辅助知识的方法。通过远程控制三指抓手和两指抓手，进行了远程操作杯子的三个主要任务的实验：使用、移动和交接。结果表明，所提出的模型有效地学习了首选辅助的知识，并且机器人之间的知识转移允许这种半自主远程操作策略以较少的培训工作量来扩大规模。讨论了在不同机器人手之间传输这种辅助知识的方法。通过远程控制三指抓手和两指抓手，进行了远程操作杯子的三个主要任务的实验：使用、移动和交接。结果表明，所提出的模型有效地学习了首选辅助的知识，并且机器人之间的知识转移允许这种半自主远程操作策略以较少的培训工作量来扩大规模。