PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning from Demonstration

This article proposes a Learning-from-Demonstra- tion (LfD) method using probability densities on the workspaces of robot manipulators. The method, named PRobabilistically- Informed Motion Primitives (PRIMP), learns the probability dis- tribution of the end effector trajectories in the 6-D workspace that includes both positions and orientations. It is able to adapt to new situations such as novel via points with uncertainty and a change of viewing frame. The method itself is robot-agnostic, in that the learned distribution can be transferred to another robot with the adaptation to its workspace density. Workspace-STOMP, a new version of the existing STOMP motion planner, is also introduced, which can be used as a postprocess to improve the performance of PRIMP and any other reachability-based LfD method. The com- bination of PRIMP and Workspace-STOMP can further help the robot avoid novel obstacles that are not present during the demon- stration process. The proposed methods are evaluated with several sets of benchmark experiments. PRIMP runs more than five times faster than existing state-of-the-art methods while generalizing trajectoriesmorethantwiceasclosetoboththedemonstrationsand novel desired poses. They are then combined with our lab’s robot imagination method that learns object affordances, illustrating the applicability to learn tool use through physical experiments.