Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, \textit{i.e.}, controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.

翻译：通过精细控制物理接触来实现物体的灵巧操作，是日常生活中许多重要任务的基础。精细接触控制的一项核心能力是柔顺控制，即在运动过程中控制接触力。对于机器人而言，目前广泛探索的方法严重依赖于被操作物体的模型以及昂贵的传感器，以获取实时控制所需的接触位置和力信息。然而，模型难以获得，传感器成本高昂，这阻碍了个人机器人在家庭和商业场所的普及。本研究采用无模型强化学习方法，在由低成本、信息贫乏的触觉传感器构建的机器人操作系统中，训练法向接触力控制器。尽管传感能力有限，我们的力控制器仍可与运动控制器结合，在物体操作过程中实现精细的接触交互。在非抓取式灵巧操作实验中，已展示出具有前景的结果。