Globally Stable Neural Imitation Policies
In this project, we developed the Stable Neural Dynamical System (SNDS) to improve imitation policies by ensuring stability of the trained policy. Our approach uses a neural policy architecture based on the Lyapunov theorem to provide formal stability guarantees. We jointly train the policy and a Lyapunov candidate to ensure global stability.
May 10, 2024
Learning Lyapunov-Stable Polynomial Dynamical Systems through Imitation
We present an approach for learning policies represented by globally stable nonlinear dynamical systems. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial's coefficients jointly with a learnable Lyapunov candidate to guarantee stability and predictability of the policy.
Oct 12, 2023