# Generating Stable and Collision-Free Policies through Lyapunov Function Learning

arXiv: https://arxiv.org/abs/2211.08976 · HTML: https://mlsanity.com/paper/2211.08976/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Globally Stable Neural Imitation Policies](https://mlsanity.com/paper/2403.04118/) (2024)
- [Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview](https://mlsanity.com/paper/2403.19916/) (2024)
- [Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks](https://mlsanity.com/paper/2410.01033/) (2024)

## Abstract (arXiv)

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.