PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning

TL;DR

PRM-RL combines reinforcement learning and sampling-based planning to enable long-range robot navigation, demonstrating significant improvements in task completion in complex environments both in simulation and real-world scenarios.

Contribution

The paper introduces PRM-RL, a hierarchical approach that integrates RL with probabilistic roadmaps for effective long-range navigation in complex environments.

Findings

PRM-RL successfully navigates 215 m indoor trajectories under noisy conditions.

Aerial cargo delivery over 1000 m without violating constraints.

Outperforms standalone RL and traditional sampling-based planners.

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.