Bimanual Rope Manipulation Skill Synthesis through Context Dependent Correction Policy Learning from Human Demonstration

Learning from demonstration (LfD) with behavior cloning is attractive for its simplicity; however, compounding errors in long and complex skills can be a hindrance. Considering a target skill as a sequence of motor primitives is helpful in this respect. Then the requirement that a motor primitive ends in a state that allows the successful execution of the subsequent primitive must be met. In this study, we focus on this problem by proposing to learn an explicit correction policy when the expected transition state between primitives is not achieved. The correction policy is learned via behavior cloning by the use of Conditional Neural Motor Primitives (CNMPs) that can generate correction trajectories in a context-dependent way. The advantage of the proposed system over learning the complete task as a single action is shown with a table-top setup in simulation, where an object has to be pushed through a corridor in two steps. Then, the applicability of the proposed method to bi-manual knotting in the real world is shown by equipping an upper-body humanoid robot with the skill of making knots over a bar in 3D space.