180 citations · 601 across the 46 of their papers we have counts for
9 papers · 1 filter
On-Robot Bayesian Reinforcement Learning for POMDPs
Hai Nguyen, Sammie Katt, Yuchen Xiao +1
Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expe…
Leveraging Fully Observable Policies for Learning under Partial Observability
Hai Nguyen, Andrea Baisero, Dian Wang +2
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state i…
Hierarchical Reinforcement Learning under Mixed Observability
Hai Nguyen, Zhihan Yang, Andrea Baisero +3
The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable while others are not. In this wor…
Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps
Chengguang Xu, Christopher Amato, Lawson L. S. Wong
In robot navigation, generalizing quickly to unseen environments is essential. Hierarchical methods inspired by human navigation have been proposed, typically consisting of a high-…
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning
Mohammadreza Sharif, Deniz Erdogmus, Christopher Amato +1
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into inte…
Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones
Roi Yehoshua, Juan Heredia-Juesas, Yushu Wu +2
Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion along with others. In this paper we de…