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Iberian Robotics conference

ROBOT 2019: Robot 2019: Fourth Iberian Robotics Conference pp 213–224Cite as

Effects of a Social Force Model Reward in Robot Navigation Based on Deep Reinforcement Learning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1093))

Abstract

In this paper is proposed an inclusion of the Social Force Model (SFM) into a concrete Deep Reinforcement Learning (RL) framework for robot navigation. These types of techniques have demonstrated to be useful to deal with different types of environments to achieve a goal. In Deep RL, a description of the world to describe the states and a reward adapted to the environment are crucial elements to get the desire behaviour and achieve a high performance. For this reason, this work adds a dense reward function based on SFM and uses the forces in the states like an additional description. Furthermore, obstacles are added to improve the behaviour of works that only consider moving agents. This SFM inclusion can offer a better description of the obstacles for the navigation. Several simulations have been done to check the effects of these modifications in the average performance.

Work supported by the Spanish Ministry of Science and Innovation under project ColRobTransp (DPI2016-78957-RAEI/FEDER EU) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Óscar Gil is also supported by Spanish Ministry of Science and Innovation under a FPI-grant, BES-2017-082126.

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Author information

Authors and Affiliations

  1. Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain

    Óscar Gil & Alberto Sanfeliu

Authors
  1. Óscar Gil
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  2. Alberto Sanfeliu
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Corresponding author

Correspondence to Óscar Gil .

Editor information

Editors and Affiliations

  1. School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Manuel F. Silva

  2. Department of Electrical Engineering, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Luís Paulo Reis

  4. UPC, Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Sanfeliu

  5. Centro Universitario de la Defensa (CUD), Zaragoza, Spain

    Danilo Tardioli

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Gil, Ó., Sanfeliu, A. (2020). Effects of a Social Force Model Reward in Robot Navigation Based on Deep Reinforcement Learning. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_18

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