Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
This work addresses the challenge of selecting appropriate task representations for robot skill learning, which is incremental as it builds on existing probabilistic and torque control methods.
The authors tackled the problem of learning robot skills from demonstrations by proposing a probabilistic method that simultaneously learns and synthesizes torque control commands, incorporating task space, joint space, and force constraints, and validated it on 7-DoF torque-controlled manipulators in two experimental scenarios.
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.