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

Understand

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.