M. Ani Hsieh | DeepAI

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∙ 09/20/2023

TOPPQuad: Dynamically-Feasible Time Optimal Path Parametrization for Quadrotors

Planning time-optimal trajectories for quadrotors in cluttered environme...

0 Katherine Mao, et al. ∙

∙ 08/16/2023

Safety Filter Design for Neural Network Systems via Convex Optimization

With the increase in data availability, it has been widely demonstrated ...

0 Shaoru Chen, et al. ∙

∙ 08/01/2023

Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots

The recent increase in data availability and reliability has led to a su...

0 Kong Yao Chee, et al. ∙

∙ 04/14/2023

EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

Event-based sensors have recently drawn increasing interest in robotic p...

3 ZiYun Wang, et al. ∙

∙ 04/03/2023

Leveraging Predictive Models for Adaptive Sampling of Spatiotemporal Fluid Processes

Persistent monitoring of a spatiotemporal fluid process requires data sa...

0 Sandeep Manjanna, et al. ∙

∙ 12/22/2022

Stochastic Nonlinear Ensemble Modeling and Control for Robot Team Environmental Monitoring

We seek methods to model, control, and analyze robot teams performing en...

0 Victoria Edwards, et al. ∙

∙ 12/19/2022

Proportional Control for Stochastic Regulation on Allocation of Multi-Robots

Any strategy used to distribute a robot ensemble over a set of sequentia...

0 Thales C. Silva, et al. ∙

∙ 12/19/2022

Receding Horizon Control on the Broadcast of Information in Stochastic Networks

This paper focuses on the broadcast of information on robot networks wit...

0 Thales C. Silva, et al. ∙

∙ 12/03/2022

Online Estimation of the Koopman Operator Using Fourier Features

Transfer operators offer linear representations and global, physically m...

0 Tahiya Salam, et al. ∙

∙ 11/24/2022

Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles

Nonlinear model predictive control (MPC) is a flexible and increasingly ...

0 Kong Yao Chee, et al. ∙

∙ 09/16/2022

LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations

State estimation is an important aspect in many robotics applications. I...

0 Kong Yao Chee, et al. ∙

∙ 08/11/2022

Adaptive Sampling of Latent Phenomena using Heterogeneous Robot Teams (ASLaP-HR)

In this paper, we present an online adaptive planning strategy for a tea...

0 Matthew Malencia, et al. ∙

∙ 07/19/2022

Online Dynamics Learning for Predictive Control with an Application to Aerial Robots

In this work, we consider the task of improving the accuracy of dynamic ...

0 Tom Z. Jiahao, et al. ∙

∙ 07/15/2022

MARLAS: Multi Agent Reinforcement Learning for cooperated Adaptive Sampling

The multi-robot adaptive sampling problem aims at finding trajectories f...

0 Lishuo Pan, et al. ∙

∙ 03/01/2022

Flow-Based Control of Marine Robots in Gyre-Like Environments

We present a flow-based control strategy that enables resource-constrain...

0 Gedaliah Knizhnik, et al. ∙

∙ 09/13/2021

Learning and Leveraging Environmental Features to Improve Robot Awareness

This paper studies how global dynamics can inform path planning and deci...

0 Tahiya Salam, et al. ∙

∙ 09/10/2021

Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations

Understanding decentralized dynamics from collective behaviors in swarms...

0 Tom Z. Jiahao, et al. ∙

∙ 09/10/2021

KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots

In this work, we consider the problem of deriving and incorporating accu...

0 Kong Yao Chee, et al. ∙

∙ 08/07/2021

Deformable Image Registration using Neural ODEs

Deformable image registration, aiming to find spatial correspondence bet...

0 Yifan Wu, et al. ∙

∙ 03/18/2021

Heterogeneous robot teams for modeling and prediction of multiscale environmental processes

This paper presents a framework to enable a team of heterogeneous mobile...

0 Tahiya Salam, et al. ∙

∙ 10/07/2020

Learning Nonlinear Dynamics and Chaos: A Universal Framework for Knowledge-Based System Identification and Prediction

We present a universal framework for learning the behavior of dynamical ...

0 Tom Z. Jiahao, et al. ∙

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