  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. First, we use deep reinforcement learning to learn
an obstacle-avoiding policy that maps a robot's sensor observations to actions,
which is used as a local planner during planning and as a controller during
execution. Second, we train a reachability estimator in a supervised manner,
which predicts the RL policy's time to reach a state in the presence of
obstacles. Lastly, we introduce RL-RRT that uses the RL policy as a local
planner, and the reachability estimator as the distance function to bias
tree-growth towards promising regions. We evaluate our method on three
kinodynamic systems, including physical robot experiments. Results across all
three robots tested indicate that RL-RRT outperforms state of the art
kinodynamic planners in efficiency, and also provides a shorter path finish
time than a steering function free method. The learned local planner policy and
accompanying reachability estimator demonstrate transferability to the
previously unseen experimental environments, making RL-RRT fast because the
expensive computations are replaced with simple neural network inference.
Video: https://youtu.be/dDMVMTOI8KY
