Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
We introduce a class of contractive imitation policies with theoretical guarantees and out-of-sample error bounds for robot learning.
Aug 22, 2024
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