Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour

TL;DR

This paper introduces a multi-task reinforcement learning approach for environment-specific robot navigation, enabling faster training and improved accuracy in diverse settings.

Contribution

It presents a novel multi-task learning framework for navigation that adapts behaviors to different environments while sharing knowledge across tasks.

Findings

26% reduction in training time

Increased navigation accuracy

Effective in both simulated and real-world environments

Abstract

Robots need to be able to work in multiple different environments. Even when performing similar tasks, different behaviour should be deployed to best fit the current environment. In this paper, We propose a new approach to navigation, where it is treated as a multi-task learning problem. This enables the robot to learn to behave differently in visual navigation tasks for different environments while also learning shared expertise across environments. We evaluated our approach in both simulated environments as well as real-world data. Our method allows our system to converge with a 26% reduction in training time, while also increasing accuracy.