Paper Info
Title
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning.
Abstract
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling-based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology. Next, the sampling-based planners provide roadmaps which connect robot configurations that can be successfully navigated by the RL agent. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. Our results show improvement in task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 m long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1000 m without violating the task constraints in an environment 63 million times larger than used in training.
Year
Venue
DocType
2018
ICRA
Conference
Volume
ISSN
Citations 
abs/1710.03937
IEEE International Conference on Robotics and Automation (ICRA), 2018
3
PageRank 
References 
Authors
0.39
13
7
Name
Order
Citations
PageRank
Aleksandra Faust16814.83
Oscar A. Ramirez231.06
Marek Fiser330.39
Kenneth Oslund430.39
Anthony Francis5163.70
James C. Davidson681.57
Lydia Tapia719424.66