An imitation learning framework with inherent stability for motion planning and its application to space manipulation tasks

As robots are required to conduct versatile manipulations in unstructured space environments, traditional planning and control strategies may become cumbersome or even infeasible. To overcome this challenge, the paper presents imitation learning with inherent Lyapunov stability (IL2S), a novel framework for jointly learning the dynamical system and accompanying Lyapunov function from demonstrations. We represent the robot motion policy as a nonlinear autonomous dynamical system that captures the invariant motion patterns from a handful of teaching examples. Furthermore, the elaborate neural networks are leveraged to simultaneously learn the motion model and the parametric control function, whereby the generated movements closely follow the demonstrations, ultimately converge to the target, and instantly respond to unanticipated changes. Our approach yields resembled trajectories on the handwriting dataset and is demonstrated extensively in real-world experiments, where the robot accomplishes two different satellite manipulation tasks, namely static grasping and dynamic docking.