52 citations · 172 across the 27 of their papers we have counts for
9 papers · 1 filter
Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey
Xuesu Xiao, Bo Liu, Garrett Warnell +1
Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigati…
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…
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…
A Lifelong Learning Approach to Mobile Robot Navigation
Bo Liu, Xuesu Xiao, Peter Stone
This paper presents a self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-sp…
Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination
Xuesu Xiao, Bo Liu, Garrett Warnell +1
While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot n…
APPLD: Adaptive Planner Parameter Learning from Demonstration
Xuesu Xiao, Bo Liu, Garrett Warnell +2
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems general…