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

2021/06/02 by Bian Xihan, Bian, Xihan, Oscar Méndez +3
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2106.01434

openalex publication_date 2021/06/02 · openalex created_date 2021/06/22 · openalex updated_date 2026/07/28

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.

Citations

• AI2-THOR: An Interactive 3D Environment for Visual AI

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