Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints

TL;DR

This paper introduces a probabilistic method for learning and synthesizing torque controllers that incorporate task, joint, and force constraints from demonstrations, enabling robots to execute complex tasks more effectively.

Contribution

It presents a novel probabilistic framework that combines multiple torque controllers based on learned relevance, improving task execution in robotic manipulators.

Findings

Successfully applied to 7-DoF manipulators

Effectively integrates multiple control strategies

Demonstrates improved task performance

Abstract

When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7-DoF torquecontrolled manipulators, with tasks that require the consideration of different controllers to be properly executed.