Bio-inspired learning and database expansion of Compliant Movement Primitives

The paper addresses the problem of learning torque primitives - the torques associated to a kinematic trajectory, and required in order to accurately track this kinematic trajectory. Learning torque primitives, which can be interpreted as internal dynamic models, is crucial to achieve at the same time (1) high tracking accuracy and (2) compliant behaviour. The latter improves the safety concerns of working in unstructured environments or with humans. In the proposed approach, first learning by demonstration is used to obtain the kinematic trajectories, which are encoded in the form of Dynamic Movement Primitives (DMPs). These are combined with the corresponding task-specific Torque Primitives (TPs), and together they form new task-related compliant movements, denoted as Compliant Movement Primitives (CMPs). Unlike the DMPs, the TPs cannot be directly acquired from user demonstrations. Inspired by the human sensorimotor learning ability, we propose a novel method which can autonomously learn task-specific Torque Primitives (TPs) associated to given kinematic trajectories in the form of DMPs. The proposed algorithm is completely autonomous, and can be used to rapidly generate and expand the database of CMPs motions. Since the CMPs are parameterized, statistical generalisation can be used to obtain an initial TP estimate of a new CMP motion. Thereby, the learning rate of new CMPs can be significantly improved. The evaluation of the proposed approach on a humanoid robot CoMan performing reaching task shows fast TP acquisition and accurate generalization estimates in real-world scenarios.