153 citations · 385 across the 25 of their papers we have counts for
19 papers · 1 filter
APPLI: Adaptive Planner Parameter Learning From Interventions
Zizhao Wang, Xuesu Xiao, Bo Liu +2
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptima…
APPLR: Adaptive Planner Parameter Learning from Reinforcement
Zifan Xu, Gauraang Dhamankar, Anirudh Nair +5
Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-t…
Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Varun Kompella, Roberto Capobianco, Stacy Jong +5
The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting…
Extended Abstract: Motion Planners Learned from Geometric Hallucination
Xuesu Xiao, Bo Liu, Peter Stone
Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-qu…
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sihang Guo, Ruohan Zhang, Bo Liu +4
Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are…
Agile Robot Navigation through Hallucinated Learning and Sober Deployment
Xuesu Xiao, Bo Liu, Peter Stone
Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous navigation, which uses training data collected in completely safe environments and adds numer…