We introduce a class of contractive imitation policies with theoretical guarantees and out-of-sample error bounds for robot learning.
Aug 22, 2024
Our paper on globally stable neural imitation policies will be a part of ICRA 2024 in Japan. Meet us at the Wednesday's poster session for more discussion if you're in Japan at that time!
Feb 16, 2024
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