Generating Stable and Collision-Free Policies through Lyapunov Function Learning
Alexandre Coulombe,Hsiu-Chin Lin,Alexandre Coulombe,Hsiu-Chin Lin
The need for rapid and reliable robot deployment is on the rise. Imitation Learning (IL) has become popular for producing motion planning policies from a set of demonstrations. However, many methods in IL are not guaranteed to produce stable policies. The generated policy may not converge to the robot target, reducing reliability, and may collide with its environment, reducing the safety of the sy...
