90 citations · 230 across the 45 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…
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
Yunshu Du, Garrett Warnell, Assefaw Gebremedhin +2
Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL) algorithms by allowing an agent to store and reuse its past experiences in a replay bu…
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…
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
Siddharth Desai, Ishan Durugkar, Haresh Karnan +3
We examine the problem of transferring a policy learned in a source environment to a target environment with different dynamics, particularly in the case where it is critical to re…
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…