Learning and generalizing force control policies for sculpting

Abstract

Abstract-Humans exhibit exceptional skills in using tools and manipulating objects of their environment by skillfully controlling exerted force and arm impedance. One of the basic components of this mechanism is the generation of internal models which associate kinematic variables with applied force. On the other hand, making robots capable of skillfully using tools and adapting their motor behavior to new environmental conditions is rather complex. In the present paper, we investigate learning of force control policies for robotic sculpting given multiple task demonstrations. These policies express the relationship between constrained motions and exerted force and are learned in Cartesian space where the coupling of dynamics between different directions of motion is also taken into account. In addition, a novel algorithm is proposed to generalize these policies to new motion tasks, executed in a sufficiently homogeneous environment, same with that in demonstrations, but in presence of new motion-dependent external forces. To this aim, a differential calculus approach is proposed where not only the mapping from motion to force but also from difference in motion to difference in force is learned to generalize the policies to new contexts. This is achieved by learning apart from a set of policy parameters, some newly introduced quantities, so called weight differentials, which express the rate of change of the policy parameters. The proposed approach is validated in simple real-world sculpting experiments by using a two degreesof-freedom haptic device.

Robotic Assembly of Timber Structures in a Human-Robot Collaboration Setup

“…For in-contact tasks, where uncertainties e.g., manufacturing tolerances and material inconsistency, play a major factor, the before mentioned framework can efficiently be exploited, when coupled with a force control strategy as shown in Rozo et al (2013), Koropouli et al (2012), andKormushev et al (2011). Moreover, adaptation can also be achieved by changing the impedance of the robot, depending on the requirements of the task.…”

Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

“…The benefits of the DMP framework can be efficiently exploited for in-contact tasks, when coupling the DMP in a force-based LbD scenario, as shown in Koropouli et al (2012) and Kormushev et al (2011). In the work of Rozo et al (2013) a two stage force LbD approach was presented, where in the first stage they recorded the positions and orientations of the desired movement and in the subsequent stage the corresponding forces and torques.…”

Reinforcement learning for improving imitated in-contact skills

“…In this paper, we study how to improve an imitated wood planing task which exhibits an extremely complex dynamic interaction; every cut will generate a new environment, which does not allow for generalization [8], [9], [10] or planning [11] of the planing task. Hence, we apply the trial and error framework of reinforcement learning (RL) for improving the performance of the imitated planing skill.…”