Search Results for author: Soon-Jo Chung

Found 23 papers, 7 papers with code

Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds

1 code implementation13 May 2022 Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli, Anima Anandkumar, Yisong Yue, Soon-Jo Chung

Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.

Meta-Learning

Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness

no code implementations18 Dec 2021 Daniel Neamati, Yashwanth Kumar Nakka, Soon-Jo Chung

Therefore, the training data domain should include spacecraft trajectories to accurately evaluate the learned model's safety and robustness.

Gaussian Processes

A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability

no code implementations2 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.

Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview

no code implementations1 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.

A Two-Part Controller Synthesis Approach for Nonlinear Stochastic Systems Perturbed by Lévy Noise Using Renewal Theory and HJB-Based Impulse Control

no code implementations22 Jul 2021 SooJean Han, Soon-Jo Chung

We are motivated by the lack of discussion surrounding methodological control design procedures for nonlinear shot and L\'evy noise stochastic systems to propose a hierarchical controller synthesis method with two parts.

Meta-Adaptive Nonlinear Control: Theory and Algorithms

1 code implementation NeurIPS 2021 Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue

We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task nonlinear control.

Multi-Task Learning Representation Learning

Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty

no code implementations5 Jun 2021 Yashwanth Kumar Nakka, Soon-Jo Chung

We also present the predictor-corrector extension (gPC-SCP$^\mathrm{PC}$) for real-time motion trajectory generation in the presence of stochastic uncertainty.

Motion Planning

H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch

no code implementations5 May 2021 Benjamin Rivière, Soon-Jo Chung

We present H-TD2: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynamic urban environment to minimize expected customer waiting times.

Decision Making

Incremental Nonlinear Stability Analysis of Stochastic Systems Perturbed by Lévy Noise

no code implementations24 Mar 2021 SooJean Han, Soon-Jo Chung

The convergence rate for shot noise systems is the same as the exponentially-stable nominal system, but with a tradeoff between the parameters of the shot noise process and the size of the error ball.

Learning-based Adaptive Control using Contraction Theory

no code implementations4 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.

Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions

no code implementations2 Mar 2021 Michael O'Connell, Guanya Shi, Xichen Shi, Soon-Jo Chung

We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.

Meta-Learning

Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach

no code implementations25 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.

Imitation Learning Motion Planning

Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions

no code implementations10 Dec 2020 Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue, Soon-Jo Chung

We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity.

Motion Planning

The Power of Predictions in Online Control

no code implementations NeurIPS 2020 Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman

We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics.

Neural Stochastic Contraction Metrics for Learning-based Control and Estimation

1 code implementation6 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.

Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach

4 code implementations8 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).

Motion Planning Optimal Motion Planning

Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization

2 code implementations8 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

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

no code implementations9 May 2020 Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung

The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.

Motion Planning Optimal Motion Planning +1

GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning

1 code implementation26 Feb 2020 Benjamin Rivière, Wolfgang Hoenig, Yisong Yue, Soon-Jo Chung

We present GLAS: Global-to-Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning.

Robotics

Online Optimization with Memory and Competitive Control

1 code implementation NeurIPS 2020 Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman

This paper presents competitive algorithms for a novel class of online optimization problems with memory.

Robust Regression for Safe Exploration in Control

no code implementations L4DC 2020 Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue

To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.

Generalization Bounds Safe Exploration

Neural Lander: Stable Drone Landing Control using Learned Dynamics

no code implementations19 Nov 2018 Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung

To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.

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