Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

TL;DR

This paper introduces a deep reinforcement learning-based mapless motion planner for mobile robots that navigates to targets without relying on obstacle maps, trained end-to-end from sparse sensor data, and effective in unseen environments.

Contribution

It presents a novel end-to-end deep reinforcement learning approach for mapless navigation, eliminating the need for obstacle maps and prior demonstrations.

Findings

Successfully navigates in virtual and real environments

Avoids obstacles without prior map information

Operates with sparse sensor inputs

Abstract

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.