Human-Robot Collaborative Navigation Search Using Social Reward Sources

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


This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents’ individual performance and qualitative surveying of participants’ perception. After that, the three proposed communication levels are compared against each other using the previous metrics.


Human-robot interaction Human-robot collaboration Human-Robot Collaborative Navigation Social reward Motion planning 



Work supported under projects ColRobTransp (DPI2016-78957-RAEI/FEDER EU), TERRINet (H2020-INFRAIA-2017-1-two-stage-730994) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institut de Robòtica i Informàtica Industrial (CSIC-UPC)BarcelonaSpain

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