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

2021-06-02 19:31:27
Xihan Bian, Oscar Mendez, Simon Hadfield
arXiv_RO
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.01434

PDF

https://arxiv.org/pdf/2106.01434.pdf

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