Search Results for author:
Found 13 papers, 3 papers with code
Safety Filter Design for Neural Network Systems via Convex Optimization
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.
EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks
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.
Leveraging Predictive Models for Adaptive Sampling of Spatiotemporal Fluid Processes
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.
Online Estimation of the Koopman Operator Using Fourier Features
Transfer operators offer linear representations and global, physically meaningful features of nonlinear dynamical systems.
Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles
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.
LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations
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.
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
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.
KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots
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.
NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration
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.
Scalable Multirobot Planning for Informed Spatial Sampling
This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields.
Critical transition for colliding swarms
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
Knowledge-Based Learning of Nonlinear Dynamics and Chaos
For the Lorenz system, different types of domain knowledge are incorporated to demonstrate the strength of knowledge embedding in data-driven system identification.
Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics
We present a data-driven modeling strategy to overcome improperly modeled dynamics for systems exhibiting complex spatio-temporal behaviors.
