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International Journal of Social Robotics

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29-11-2021

Crowd-Comfort Robot Navigation Among Dynamic Environment Based on Social-Stressed Deep Reinforcement Learning

Authors: Zhengxi Hu, Yingli Zhao, Sen Zhang, Lei Zhou, Jingtai Liu

Published in: International Journal of Social Robotics | Issue 4/2022

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Abstract

Robot navigation in a dynamic environment is a challenging task because not only the safety but also the comfort of surrounding pedestrians shall be necessarily considered. This paper proposes the concept of social stress based on tension space of robot and human, which is an important part of Human-Robot interaction. Especially, the proposed approach develops crowd-comfort navigation by combining social stress indexes with a deep reinforcement learning framework and the value network. A set of typical simulation experiments show that our method improves the comfort of surrounding pedestrians effectively during the process of robot navigation. In addition, the fine-tuned technology proposed in this paper has also been proven to be suitable for different scenarios.

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Project webpage, https://github.com/vita-epfl/CrowdNav

Title: Crowd-Comfort Robot Navigation Among Dynamic Environment Based on Social-Stressed Deep Reinforcement Learning
Authors: Zhengxi Hu
Yingli Zhao
Sen Zhang
Lei Zhou
Jingtai Liu
Publication date: 29-11-2021
Publisher: Springer Netherlands
Published in: International Journal of Social Robotics / Issue 4/2022
Print ISSN: 1875-4791
Electronic ISSN: 1875-4805
DOI: https://doi.org/10.1007/s12369-021-00838-x

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