Receding horizon optimization of robot motions generated by hierarchical movement primitives

This paper introduces a motion generation framework that integrates a hierarchical movement primitive (MP) layer with optimal control in form of receding horizon optimization. In order to benefit from fast reactions on the MP-layer, the optimal control layer can be overridden in risky situations to generate quick, though non-optimal solutions. By this, the system fulfills four desirable properties. It continuously adapts the robot's motion without noticeable delay (1) by optimizing for collision and joint limit avoidance based on a future time horizon instead of the current state only (2). It accounts for the full robot motion that may result from multiple active MPs at the same time (3) and despite a possibly slow optimization still provides the robustness and quick reaction capabilities of MPs (4). The framework has been validated in an experiment in which a humanoid robot performed a task, optimized wrt. collisions and joint limit avoidance, but still could react within 50 ms after detection of a potential risk.