Extrinsic Dexterity Through Active Slip Control Using Deep Predictive Models

Abstract

We present a machine learning methodology for actively controlling slip, in order to increase robot dexterity. Leveraging recent insights in deep learning, we propose a Deep Predictive Model that uses tactile sensor information to reason about slip and its future influence on the manipulated object. The obtained information is then used to precisely manipulate objects within a robot end-effector using external perturbations imposed by gravity or acceleration. We show in a set of experiments that this approach can be used to increase a robot's repertoire of motor skills.