no code implementations • 13 Jul 2023 • Hiroyasu Tsukamoto, Benjamin Rivière, Changrak Choi, Amir Rahmani, Soon-Jo Chung
First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy.
no code implementations • 26 Oct 2022 • Benjamin P. S. Donitz, Declan Mages, Hiroyasu Tsukamoto, Peter Dixon, Damon Landau, Soon-Jo Chung, Erica Bufanda, Michel Ingham, Julie Castillo-Rogez
Interstellar objects (ISOs) are fascinating and under-explored celestial objects, providing physical laboratories to understand the formation of our solar system and probe the composition and properties of material formed in exoplanetary systems.
no code implementations • 9 Aug 2022 • Hiroyasu Tsukamoto, Soon-Jo Chung, Benjamin Donitz, Michel Ingham, Declan Mages, Yashwanth Kumar Nakka
In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees.
no code implementations • 2 Oct 2021 • Hiroyasu Tsukamoto, Soon-Jo Chung, Jean-Jacques Slotine, Chuchu Fan
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 function, the existence of which is a necessary and sufficient condition for incremental exponential stability of non-autonomous nonlinear system trajectories.
no code implementations • 1 Oct 2021 • 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 uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other.
no code implementations • 4 Mar 2021 • 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.
no code implementations • 25 Feb 2021 • Hiroyasu Tsukamoto, Soon-Jo Chung
This paper presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory.
1 code implementation • 6 Nov 2020 • 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.
2 code implementations • 8 Jun 2020 • Hiroyasu Tsukamoto, Soon-Jo Chung
For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases.
Systems and Control Robotics Systems and Control
4 code implementations • 8 Jun 2020 • Hiroyasu Tsukamoto, Soon-Jo Chung
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM).