140 citations · 337 across the 15 of their papers we have counts for
15 papers
Conformal Koopman for Embedded Nonlinear Control with Statistical Robustness: Theory and Real-World Validation
Koki Hirano, Hiroyasu Tsukamoto
We propose a fully data-driven, Koopman-based framework for statistically robust control of discrete-time nonlinear systems with linear embeddings. Establishing a connection betwee…
Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems
Rihan Aaron D'Silva, Hiroyasu Tsukamoto
This paper presents novel method for distribution-free robust trajectory optimization and control of discrete-time, nonlinear, and non-Gaussian stochastic systems, with closed-loop…
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…
Regret-Optimal Defense Against Stealthy Adversaries: A System Level Approach
Hiroyasu Tsukamoto, Joudi Hajar, Soon-Jo Chung +1
Modern control designs in robotics, aerospace, and cyber-physical systems rely heavily on real-world data obtained through system outputs. However, these outputs can be compromised…
Robust Optimal Network Topology Switching for Zero Dynamics Attacks
Hiroyasu Tsukamoto, Joshua D. Ibrahim, Joudi Hajar +3
The intrinsic, sampling, and enforced zero dynamics attacks (ZDAs) are among the most detrimental stealthy attacks in robotics, aerospace, and cyber-physical systems. They exploit…
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
Hiroyasu Tsukamoto, Benjamin Rivière, Changrak Choi +2
The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-base…
Interstellar Object Accessibility and Mission Design
Benjamin P. S. Donitz, Declan Mages, Hiroyasu Tsukamoto +6
Interstellar objects (ISOs) represent a compelling and under-explored category of celestial bodies, providing physical laboratories to understand the formation of our solar system…
Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
Hiroyasu Tsukamoto, Soon-Jo Chung, Yashwanth Kumar Nakka +3
Interstellar objects (ISOs) are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with gen…
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…
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach
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 form…
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…
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
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). The NCM uses a deep long…
Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization
Hiroyasu Tsukamoto, Soon-Jo Chung
This paper presents ConVex optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM), a new state feedback control framework for a class of Ito stochastic no…