  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.
