Active Tactile Exploration Based on Cost-Aware Information Gain Maximization

Abstract

Active tactile perception is a powerful mechanism to collect contact information by touching an unknown object with a robot finger in order to enable further interaction with the object or grasping of the object. The acquired object knowledge can be used to build object shape models based on such usually sparse tactile contact information. In this paper, we address the problem of object shape reconstruction from sparse tactile data gained from a robot finger that yields contact information and surface orientation at the contact points. To this end, we present an exploration algorithm which determines the next best touch target in order to maximize the estimated information gain and to minimize the expected costs of exploration actions. We introduce the Information Gain Estimation Function (IGEF), which combines different goals as measure for the quantification of the cost-aware information gain during exploration. The IGEF-based exploration strategy is validated in simulation using 48 publicly available object models and compared to state-of-the-art Gaussian processes-based exploration approaches. The results show the performance of the approach regarding exploration efficiency, cost-awareness and suitability for application in real tactile sensing scenarios.