RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies

10 Jul 2019Hao-Tien Lewis ChiangJasmine HsuMarek FiserLydia TapiaAleksandra Faust

This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning... (read more)

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