Name
Title | ||
|---|---|---|
Rl-Rrt: Kinodynamic Motion Planning Via Learning Reachability Estimators From Rl Policies |
Abstract | ||
|---|---|---|
This letter 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LRA.2019.2931199 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | Field | DocType |
Motion and path planning, learning and adaptive systems, deep learning in robotics and automation | Motion planning,Mathematical optimization,Control theory,Control theory,Metric (mathematics),Reachability,Engineering,Robot,Artificial neural network,Estimator,Reinforcement learning | Journal |
Volume | Issue | ISSN |
4 | 4 | 2377-3766 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Tien Lewis Chiang | 1 | 16 | 4.71 |
| Jasmine Hsu | 2 | 15 | 3.39 |
| Marek Fiser | 3 | 29 | 3.66 |
| Lydia Tapia | 4 | 194 | 24.66 |
| Aleksandra Faust | 5 | 68 | 14.83 |