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

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