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The need for rapid and reliable robot deployment is on the rise. ImitationLearning (IL) has become popular for producing motion planning policies from aset of demonstrations. However, many methods in IL are not guaranteed toproduce stable policies. The generated policy may not converge to the robottarget, reducing reliability, and may collide with its environment, reducingthe safety of the system. Stable Estimator of Dynamic Systems (SEDS) producesstable policies by constraining the Lyapunov stability criteria duringlearning, but the Lyapunov candidate function had to be manually selected. Inthis work, we propose a novel method for learning a Lyapunov function and apolicy using a single neural network model. The method can be equipped with anobstacle avoidance module for convex object pairs to guarantee no collisions.We demonstrated our method is capable of finding policies in several simulationenvironments and transfer to a real-world scenario.

Generating Stable and Collision-Free Policies through Lyapunov Function Learning