APPLR: Adaptive Planner Parameter Learning from Reinforcement
Zifan Xu Gauraang Dhamankar Anirudh Nair Xuesu Xiao Garrett Warnell Bo Liu Zizhao Wang Peter Stone Zifan Xu Gauraang Dhamankar Anirudh Nair Xuesu Xiao Garrett Warnell Bo Liu Zizhao Wang Peter Stone
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-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. Howev...