Francis, Anthony, Perez D'arpino, Claudia, Li, Chengshu, Xia, Fei, Alahi, Alexandre, Alami, Rachid, Bera, Aniket, Biswas, Abhijat, Biswas, Joydeep, Chandra, Rohan, Chiang, Hao-Tien Lewis, Everett, Michael, Ha, Sehoon, Hart, Justin, How, Jonathan P., Karnan, Haresh, Lee, Tsang-Wei Edward, Manso, Luis J., Mirsky, Reuth, Pirk, Soren, Singamaneni, Phani Teja, Stone, Peter, Taylor, Ada V., Trautman, Peter, Tsoi, Nathan, Vazquez, Marynel, Xiao, Xuesu, Xu, Peng, Yokoyama, Naoki, Toshev, Alexander and Martin-Martin, Roberto (2025). Principles and Guidelines for Evaluating Social Robot Navigation Algorithms. Transactions on Human-Robot Interaction, 14 (2),
Abstract
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms that tackle social navigation remains hard because it involves not just robotic agents moving in static environments but also dynamic human agents and their perceptions of the appropriateness of robot behavior. In contrast, clear, repeatable, and accessible benchmarks have accelerated progress in fields like computer vision, natural language processing and traditional robot navigation by enabling researchers to fairly compare algorithms, revealing limitations of existing solutions and illuminating promising new directions. We believe the same approach can benefit social navigation. In this paper, we pave the road towards common, widely accessible, and repeatable benchmarking criteria to evaluate social robot navigation. Our contributions include (a) a definition of a socially navigating robot as one that respects the principles of safety, comfort, legibility, politeness, social competency, agent understanding, proactivity, and responsiveness to context, (b) guidelines for the use of metrics, development of scenarios, benchmarks, datasets, and simulators to evaluate social navigation, and (c) a design of a social navigation metrics framework to make it easier to compare results from different simulators, robots and datasets.
Publication DOI: | https://doi.org/10.1145/3700599 |
---|---|
Divisions: | College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies College of Engineering & Physical Sciences Aston University (General) |
Additional Information: | Copyright © 2025 held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. |
Uncontrolled Keywords: | social navigation,Artificial Intelligence,Human-Computer Interaction |
Publication ISSN: | 2573-9522 |
Last Modified: | 06 Mar 2025 08:11 |
Date Deposited: | 17 Jan 2025 12:32 |
Full Text Link: | |
Related URLs: |
https://dl.acm. ... 10.1145/3700599
(Publisher URL) |
PURE Output Type: | Article |
Published Date: | 2025-02-20 |
Published Online Date: | 2024-12-27 |
Accepted Date: | 2024-06-20 |
Submitted Date: | 2023-11-27 |
Authors: |
Francis, Anthony
Perez D'arpino, Claudia Li, Chengshu Xia, Fei Alahi, Alexandre Alami, Rachid Bera, Aniket Biswas, Abhijat Biswas, Joydeep Chandra, Rohan Chiang, Hao-Tien Lewis Everett, Michael Ha, Sehoon Hart, Justin How, Jonathan P. Karnan, Haresh Lee, Tsang-Wei Edward Manso, Luis J. ( ![]() Mirsky, Reuth Pirk, Soren Singamaneni, Phani Teja Stone, Peter Taylor, Ada V. Trautman, Peter Tsoi, Nathan Vazquez, Marynel Xiao, Xuesu Xu, Peng Yokoyama, Naoki Toshev, Alexander Martin-Martin, Roberto |
Download
![[img]](https://publications.aston.ac.uk/style/images/fileicons/text.png)
Version: Accepted Version
Access Restriction: Restricted to Repository staff only
License: ["licenses_description_unspecified" not defined]
![[img]](https://publications.aston.ac.uk/style/images/fileicons/text.png)
Version: Published Version
License: Creative Commons Attribution