Generating Stable and Collision-Free Policies through Lyapunov Function Learning

TL;DR

This paper introduces a neural network-based method for learning stable, collision-free policies for robots by automatically learning Lyapunov functions, improving safety and reliability in motion planning.

Contribution

A novel approach that learns Lyapunov functions and policies simultaneously with a neural network, incorporating obstacle avoidance for safer robot deployment.

Findings

Successfully finds policies in simulation environments

Transfers policies to real-world scenarios

Guarantees collision avoidance with obstacle module

Abstract

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 system. Stable Estimator of Dynamic Systems (SEDS) produces stable policies by constraining the Lyapunov stability criteria during learning, but the Lyapunov candidate function had to be manually selected. In this work, we propose a novel method for learning a Lyapunov function and a policy using a single neural network model. The method can be equipped with an obstacle avoidance module for convex object pairs to guarantee no collisions. We demonstrated our method is capable of finding policies in several simulation environments and transfer to a real-world scenario.