Robotic Haptic Exploration of Object Shape With Autonomous Symmetry Detection

Abstract

Haptic robotic exploration aims to control the movements of a robot with the objective of touching an object and retrieving physical information about it. In this work, we present an innovative exploration strategy to simultaneously detect symmetries in a 3-D object and use this information to enhance shape estimation. This is achieved by leveraging a novel formulation of Gaussian process models that allows the modeling of symmetric surfaces. Our procedure does not assume any prior knowledge about the object, neither about its shape nor about the presence and type of symmetry, necessitating only an approximate estimate of the size and boundaries (bounding box). We report experimental results both in simulation and in the real world, showing that using symmetric models leads to a reduction in shape estimation error, exploration time, and in the number of physical contacts performed by a robot when exploring objects that have symmetries.