3 citations · 7 across the 10 of their papers we have counts for
11 papers
Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow
Minh Nguyen
Many real-world control problems, ranging from finance to robotics, evolve in continuous time with non-uniform, event-driven decisions. Standard discrete-time reinforcement learnin…
Scaling Single Human Demonstrations for Imitation Learning using Generative Foundational Models
Nick Heppert, Minh Quang Nguyen, Abhinav Valada
Imitation learning is a popular paradigm to teach robots new tasks, but collecting robot demonstrations through teleoperation or kinesthetic teaching is tedious and time-consuming.…
GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments
Aditya Sai Ellendula, Yi Wang, Minh Nguyen +1
We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping(SNAM) in unknown environments. A SNAM problem is challenging as it needs t…
Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan, Yi Wang, Ryan Farell +2
We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesia…
Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL
Wen-Tse Chen, Minh Nguyen, Zhongyu Li +2
This work addresses the challenge of enabling a team of quadrupedal robots to collaboratively tow a cable-connected load through cluttered and unstructured environments while avoid…
Low-cost Robust Night-time Aerial Material Segmentation through Hyperspectral Data and Sparse Spatio-Temporal Learning
Chandrajit Bajaj, Minh Nguyen, Shubham Bhardwaj
Material segmentation is a complex task, particularly when dealing with aerial data in poor lighting and atmospheric conditions. To address this, hyperspectral data from specialize…
Generalized knowledge-enhanced framework for biomedical entity and relation extraction
Minh Nguyen, Phuong Le
In recent years, there has been an increasing number of frameworks developed for biomedical entity and relation extraction. This research effort aims to address the accelerating gr…
A Differential and Pointwise Control Approach to Reinforcement Learning
Minh Nguyen, Chandrajit Bajaj
Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing due to poor sample efficiency and lack of pathwise physical consistency. W…
Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
Chandrajit Bajaj, Minh Nguyen
Despite extensive research, time series classification and forecasting on noisy data remain highly challenging. The main difficulties lie in finding suitable mathematical concepts…
Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
Chandrajit Bajaj, Minh Nguyen, Conrad Li
In this paper, we present a novel reinforcement learning framework designed to optimize molecular dynamics by focusing on the entire trajectory rather than just the final molecular…
Physics-informed neural networks via stochastic Hamiltonian dynamics learning
Chandrajit Bajaj, Minh Nguyen
In this paper, we propose novel learning frameworks to tackle optimal control problems by applying the Pontryagin maximum principle and then solving for a Hamiltonian dynamical sys…