Shared Task Representation for Human-Robot Collaborative Navigation: The Collaborative Search Case

Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human-robot 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-robot 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-robot 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-robot team and test the acceptability and utility of different communication interface designs.