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Human-Robot Collaborative Navigation Search Using Social Reward Sources

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

Abstract

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.

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Notes

  1. 1.

    http://www.iri.upc.edu/research/webprojects/pau/datasets/BRL/.

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Acknowledgements

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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Correspondence to Marc Dalmasso .

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Dalmasso, M., Garrell, A., Jiménez, P., Sanfeliu, A. (2020). Human-Robot Collaborative Navigation Search Using Social Reward Sources. 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_8

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