Learning Sample-Efficient Target Reaching for Mobile Robots

TL;DR

This paper introduces a hierarchical, self-supervised learning architecture for mobile robots that enables efficient goal-reaching navigation using only sparse sensor data, significantly improving learning speed over existing methods.

Contribution

The paper presents a novel hierarchical architecture with a modified policy gradient algorithm that enhances sample efficiency for goal-reaching tasks in mobile robots.

Findings

Achieves goal reaching significantly faster than current state-of-the-art algorithms.

Effective navigation using only sparse range-finder measurements.

Validated through simulation and real-world experiments.

Abstract

In this paper, we propose a novel architecture and a self-supervised policy gradient algorithm, which employs unsupervised auxiliary tasks to enable a mobile robot to learn how to navigate to a given goal. The dependency on the global information is eliminated by providing only sparse range-finder measurements to the robot. The partially observable planning problem is addressed by splitting it into a hierarchical process. We use convolutional networks to plan locally, and a differentiable memory to provide information about past time steps in the trajectory. These modules, combined in our network architecture, produce globally consistent plans. The sparse reward problem is mitigated by our modified policy gradient algorithm. We model the robots uncertainty with unsupervised tasks to force exploration. The novel architecture we propose with the modified version of the policy gradient algorithm allows our robot to reach the goal in a sample efficient manner, which is orders of magnitude faster than the current state of the art policy gradient algorithm. Simulation and experimental results are provided to validate the proposed approach.