Robot Learning from Demonstration with Tactile Signals for Geometry-Dependent Tasks

Abstract

Deploying robot learning frameworks in unconstrained environments requires robustness and tractability. We must not only equip the robot with a sufficient range of sensing capabilities, but also provide training data in a sample-efficient manner. To this end, we identify and address a need specifically in robot learning from demonstration (LfD) literature to account for not only end-effector pose and wrench signals, but also tactile signals for contact. While traditional pose and wrench signals have proven to be sufficient for robots to learn basic position and force-control behaviors, they are inherently too constraining for the learning of general manipulation tasks. In particular, useful manipulation tasks often rely on the geometry of the contact interaction. To explore the value of geometry-based tactile signals, we utilize a LfD framework built upon hidden Markov models and Gaussian mixture regression, adapt it to our robotic system equipped with a soft tactile sensor, and validate its performance with an edge-following task and a manipulation task involving different object geometries.