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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Advs in Intelligent Syst., Computing



    Conference:

    Iberian Robotics conference ; 2019 ; Porto, Portugal November 20, 2019 - November 22, 2019



    Publication date :

    2019-11-20


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Deep Reinforcement Learning for Mobile Robot Navigation

    Gromniak, Martin / Stenzel, Jonas | BASE | 2019

    Free access

    Human-Robot Collaborative Navigation Search Using Social Reward Sources

    Dalmasso, Marc / Garrell, Anaís / Jiménez, Pablo et al. | TIBKAT | 2020


    Human-Robot Collaborative Navigation Search Using Social Reward Sources

    Dalmasso, Marc / Garrell, Anaís / Jiménez, Pablo et al. | Springer Verlag | 2019