When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration spaces). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task, joint 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 torque-controlled manipulators, with tasks requiring the fusion of different controllers to be properly executed.