13 citations · 30 across the 11 of their papers we have counts for
8 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…
Learning from Pixels with Expert Observations
Minh-Huy Hoang, Long Dinh, Hai Nguyen
In reinforcement learning (RL), sparse rewards can present a significant challenge. Fortunately, expert actions can be utilized to overcome this issue. However, acquiring explicit…
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…
Multi-directional Bicycle Robot for Steel Structure Inspection
Son Thanh Nguyen, Hai Nguyen, Son Tien Bui +2
This paper presents a novel design of a multi-directional bicycle robot, which targets inspecting general ferromagnetic structures including complex-shaped structures. The locomoti…
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
Hai Nguyen, Brett Daley, Xinchao Song +2
Many important robotics problems are partially observable in the sense that a single visual or force-feedback measurement is insufficient to reconstruct the state. Standard approac…