140 citations · 337 across the 15 of their papers we have counts for
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Contraction-Aware Reinforcement Learning for Nonlinear Control with Statistical Robustness
Minjae Cho, Hiroyasu Tsukamoto, Huy T. Tran
Control contraction metrics (CCMs)-defined by Riemannian metrics under which a closed-loop system is incrementally exponentially stable-offer a constructive framework for synthesiz…
A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine +1
This paper presents a theoretical overview of a Neural Contraction Metric (NCM): a neural network model of an optimal contraction metric and corresponding differential Lyapunov fun…
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniforml…
Learning-based Adaptive Control using Contraction Theory
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine
Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance. This paper thus presents a deep learning-based adaptive con…
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques E. Slotine
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems. It us…