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

## TL;DR

This paper introduces RL-RRT, a kinodynamic motion planning method combining reinforcement learning and reachability estimation to improve planning efficiency and path quality, demonstrated on multiple robotic systems.

## Contribution

It presents a novel integration of learned obstacle-avoiding policies and reachability estimators within an RRT framework for kinodynamic planning.

## Key findings

- RL-RRT outperforms existing kinodynamic planners in efficiency.
- The method achieves shorter path finish times.
- The learned policies and estimators transfer well to unseen environments.

## Abstract

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