Active Tactile Exploration using Shape-Dependent Reinforcement Learning

Tactile signals provide rich information about objects via touch and are essential for a robot to perform dex-terous manipulation. Exploring actively via tactile perception collects important information about the workspace. However, designing an effective tactile exploration policy is challenging in unstructured environments. Typically, the geometric information is incomplete, and need to be completed by actively and repeatedly interacting with the environment. In this paper, we address the tactile exploration problem by proposing a shape-information-dependent exploration strategy, which consists of two components: (1) a Shape-Belief Encoder that encodes the explored area by learning effective 3-D reconstruction and predicts the complete object shape; (2) a shape-dependent exploration policy which incorporates the encoding in (1) to plan an exploration trajectory. The policy actively acquires new information about object surface by executing exploration actions. The Shape-Belief Encoder leverages the newly collected contact points to update the surface model and guides future exploration. We validate the proposed algorithm on simulated and real robots.