aiqbench analysis
This research advances social navigation algorithms for autonomous tour-guide robots by integrating a force-based model that accounts for user position, heading, and obstacles to enhance perceived safety and intelligence. The study empirically evaluates these improvements through controlled experiments comparing the proposed model against standard A*-search baselines in dynamic environments with distractions.
Social Navigation for Tour-guide Robot
arXiv:2610.05455v1 Announce Type: new Abstract: We propose a force-based model for social navigation of a tour-guide robot. Social forces due to various factors like obstacles, user position and heading, have been accounted for in the model. We claim that each one of these forces makes the robot more sociable to the user and we design an experimental setup for evaluation. In the experiment, the user follows an autonomous robot to a destination in a known map, while undertaking a few simple sub-tasks in the middle, which serve as distractions. For each participant, we run several rounds of the experiment, each with different forces and a shortest path, A*-search baseline model. Using per-round subjective indicators, we propose to study the effect of our force model on constructs such as: follow-ability, perceived safety, and perceived intelligence.
Abstract:We propose a force-based model for social navigation of a tour-guide robot. Social forces due to various factors like obstacles, user position and heading, have been accounted for in the model. We claim that each one of these forces makes the robot more sociable to the user and we design an experimental setup for evaluation. In the experiment, the user follows an autonomous robot to a destination in a known map, while undertaking a few simple sub-tasks in the middle, which serve as distractions. For each participant, we run several rounds of the experiment, each with different forces and a shortest path, A*-search baseline model. Using per-round subjective indicators, we propose to study the effect of our force model on constructs such as: follow-ability, perceived safety, and perceived intelligence.
5 pages, 8 figures. Accepted at the 5th Workshop on Social Robot Navigation, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026
Robotics (cs.RO); Human-Computer Interaction (cs.HC)
arXiv:2610.05455 [cs.RO]
(or arXiv:2610.05455v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.05455 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 172 words
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