1 code implementation • 16 Aug 2023 • Shaoru Chen, Kong Yao Chee, Nikolai Matni, M. Ani Hsieh, George J. Pappas
With the increase in data availability, it has been widely demonstrated that neural networks (NN) can capture complex system dynamics precisely in a data-driven manner.
no code implementations • 14 Apr 2023 • ZiYun Wang, Fernando Cladera Ojeda, Anthony Bisulco, Daewon Lee, Camillo J. Taylor, Kostas Daniilidis, M. Ani Hsieh, Daniel D. Lee, Volkan Isler
Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers.
no code implementations • 3 Apr 2023 • Sandeep Manjanna, Tom Z. Jiahao, M. Ani Hsieh
Our algorithm makes use of the predictions from a learned prediction model to plan a path for an autonomous vehicle to adaptively and efficiently survey the region of interest.
no code implementations • 3 Dec 2022 • Tahiya Salam, Alice Kate Li, M. Ani Hsieh
Transfer operators offer linear representations and global, physically meaningful features of nonlinear dynamical systems.
no code implementations • 24 Nov 2022 • Kong Yao Chee, M. Ani Hsieh, Nikolai Matni
We show that the KNODE ensemble provides more accurate predictions and illustrate the efficacy and closed-loop performance of the proposed nonlinear MPC framework using two case studies.
no code implementations • 16 Sep 2022 • Kong Yao Chee, M. Ani Hsieh
In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the dynamics model used in state estimation algorithms.
1 code implementation • 19 Jul 2022 • Tom Z. Jiahao, Kong Yao Chee, M. Ani Hsieh
To improve the adaptiveness of the model and the controller, we propose an online dynamics learning framework that continually improves the accuracy of the dynamic model during deployment.
no code implementations • 10 Sep 2021 • Kong Yao Chee, Tom Z. Jiahao, M. Ani Hsieh
Using a quadrotor, we benchmark our hybrid model against a state-of-the-art Gaussian Process (GP) model and show that the hybrid model provides more accurate predictions of the quadrotor dynamics and is able to generalize beyond the training data.
no code implementations • CVPR 2022 • Yifan Wu, Tom Z. Jiahao, Jiancong Wang, Paul A. Yushkevich, M. Ani Hsieh, James C. Gee
Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis.
no code implementations • 20 May 2021 • Sandeep Manjanna, M. Ani Hsieh, Gregory Dudek
This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields.
no code implementations • 5 Jan 2021 • Jason Hindes, Victoria Edwards, M. Ani Hsieh, Ira B. Schwartz
Using this approach we are able to predict the critical swarm-on-swarm interaction coupling, below which two colliding swarms merely scatter, for near head-on collisions as a function of control parameters.
Pattern Formation and Solitons
1 code implementation • 7 Oct 2020 • Tom Z. Jiahao, M. Ani Hsieh, Eric Forgoston
For the Lorenz system, different types of domain knowledge are incorporated to demonstrate the strength of knowledge embedding in data-driven system identification.
no code implementations • 23 Aug 2020 • Maan Qraitem, Dhanushka Kularatne, Eric Forgoston, M. Ani Hsieh
We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors.