34 citations · 79 across the 31 of their papers we have counts for
7 papers · 2 filters
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…
Stochastic Grounded Action Transformation for Robot Learning in Simulation
Siddharth Desai, Haresh Karnan, Josiah P. Hanna +2
Robot control policies learned in simulation do not often transfer well to the real world. Many existing solutions to this sim-to-real problem, such as the Grounded Action Transfor…
Reinforced Grounded Action Transformation for Sim-to-Real Transfer
Haresh Karnan, Siddharth Desai, Josiah P. Hanna +2
Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the reality gap). Some exis…
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…