Active Slip Control for In-Hand Object Manipulation using Deep Predictive Models


We discuss 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. We show in a set of experiments that this approach can be used to increase a robot’s repertoire of skill.

Robotics: Science and Systems