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Many show considerable advances, but the human\u2013robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human\u2013robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human\u2013robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human\u2013robot team and test the acceptability and utility of different communication interface designs.\n<\/jats:p>","DOI":"10.1007\/s12369-023-01067-0","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T16:02:05Z","timestamp":1698681725000},"page":"145-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Shared Task Representation for Human\u2013Robot Collaborative Navigation: The Collaborative Search Case"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1734-7863","authenticated-orcid":false,"given":"Marc","family":"Dalmasso","sequence":"first","affiliation":[]},{"given":"J. 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