pith. sign in

arxiv: 2605.26710 · v1 · pith:OXOV4GKEnew · submitted 2026-05-26 · 💻 cs.RO

Look Further: Socially-Compliant Navigation System in Residential Buildings

Akira Shiba , Marina Obata , Nathan Kau , Zoltan Beck , Rishi Shah , Michael Sudano , Sabrina Lee This is my paper

Pith reviewed 2026-06-29 16:57 UTC · model grok-4.3

classification 💻 cs.RO
keywords social navigationmobile robothuman-robot interactionproactive lane changeresidential hallwaydelivery robotuser study
0
0 comments X

The pith

A delivery robot improves human ratings of safety, smoothness and politeness by shifting lanes at eight meters from oncoming people in straight hallways.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether a mobile delivery robot in residential hallways can create better human impressions by reacting to people much earlier than typical social navigation systems allow. Instead of waiting until a person is only a few meters away, the robot begins a lateral lane change when still more than eight meters distant. A study with 42 participants found that this proactive pattern raised scores on safety, smoothness, and politeness in straight frontal approaches compared with slowing, stopping, or reacting only at close range. The same pattern showed no reliable advantage at blind corners.

Core claim

By extending the robot's reaction distance beyond eight meters and using a proactive lane-change motion pattern, the robot produces measurably higher participant ratings on safety, smoothness, and politeness in straight hallway encounters than conventional short-range behaviors such as slowing down, stopping, or collision avoidance near the person.

What carries the argument

Proactive Lane-Changing (PLC) pattern that moves the robot from the hallway center to the side when an oncoming person is detected at an eight-meter distance.

If this is right

  • Residential delivery robots can improve perceived service quality by planning lateral shifts well before personal-space distances.
  • Standard reactive methods that act only near a person are outperformed in straight paths by earlier lateral repositioning.
  • Intersection scenarios require additional motion cues because early lane changes alone do not produce consistent preference gains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Human judgments of robot motion appear to incorporate anticipation over distances larger than physical interaction ranges.
  • Long-range sensing and prediction may become necessary components for socially acceptable indoor navigation systems.
  • Preference diversity at corners suggests that environmental geometry interacts with timing to shape perception.

Load-bearing premise

Observed rating differences arise from the eight-meter reaction distance and lane-change timing rather than from uncontrolled differences in robot speed, lighting, or participant expectations.

What would settle it

A follow-up study that matches speed profiles, lighting, and participant instructions exactly across conditions and finds no rating difference between the eight-meter lane change and the short-range baselines.

Figures

Figures reproduced from arXiv: 2605.26710 by Akira Shiba, Marina Obata, Michael Sudano, Nathan Kau, Rishi Shah, Sabrina Lee, Zoltan Beck.

Figure 1
Figure 1. Figure 1: Smart Logistics Robot dimensions and HRI sensors [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System diagram of proposed navigation system. Images [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: PLC behavior during hallway encounter. The top image [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Study layout in the two scenarios: Frontal Approach (right) and Blind Corner (left). Three of the 4 tasks (1, 2, and 3) follow the trajectory marked with black, while task 4 (and 4 ′ ) follow the one in gray (more details in Table I). Blue color represents the robot and human at the start of their interaction, while orange at the end of their interaction for Task 4 (and 4 ′ ). a total distance of 15 m, int… view at source ↗
Figure 7
Figure 7. Figure 7: Out of the forty-two participants, one participant [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Subjective evaluation on safety, smoothness, and politeness across [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of movement efficiency across four be [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

The distance at which a mobile robot reacts to a person strongly impacts various qualities of the human-robot interaction. In this paper, we focus on the navigation of a mobile delivery robot platform in a residential indoor hallway environment. Social navigation methods typically focus on avoiding uncomfortable human-robot interactions, such as when a robot encroaches on someone's personal space. Since personal space has been shown to be in the range of just a few meters, social navigation methods typically focus on deconflicting and resolving these short-range interactions. In this work, however, we demonstrate that by extending the reaction distance to over eight meters, far beyond the typical interaction distance, we can improve the human's perception of the robot's motion. We introduce the Proactive Lane-Changing (PLC) motion pattern and a navigation system that leverages it to react to people at an increased distance. This pattern consists of changing the robot's lateral position as it navigates down the hallway from the center to the side at an eight-meter distance from an oncoming person. We conducted a user study with 42 participants to assess their impressions of the delivery robot based on three service objectives: safety, smoothness, and politeness. In the straight hallway scenario (Frontal Approach), results showed significant improvement in each of these three objectives compared to typical motion patterns found in the literature: slowing down, stopping, and reactive collision avoidance in the proximity of a person. In contrast, in the intersection (Blind Corner) scenarios, none of the approaches performed significantly better than any other, with participants having a diverse range of preferences among robot motion patterns.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that extending a mobile delivery robot's reaction distance to over eight meters via a Proactive Lane-Changing (PLC) pattern—shifting laterally from hallway center to side—improves human perceptions of safety, smoothness, and politeness in residential indoor navigation. This is contrasted with typical short-range methods (slowing down, stopping, reactive collision avoidance). A 42-participant user study reports statistically significant gains for PLC in the straight-hallway (Frontal Approach) scenario across all three objectives, but no significant differences among approaches in blind-corner (intersection) scenarios.

Significance. If the central empirical claim holds after clarification of controls, the work supplies concrete evidence that long-range proactive behaviors can outperform conventional short-range social navigation in hallway settings. The 42-participant study with explicit service objectives (safety/smoothness/politeness) and scenario differentiation is a clear strength, providing falsifiable, human-centered data that could guide future navigation algorithm design in shared indoor spaces.

major comments (2)
  1. [User Study section] User Study section: the abstract and results paragraph report 'significant improvement' in the three objectives for the Frontal Approach but supply no information on the statistical tests used, power analysis, counterbalancing of conditions, or exact participant motion parameters. These omissions are load-bearing because they prevent independent verification that the observed rating differences are attributable to the eight-meter PLC reaction distance rather than study design artifacts.
  2. [User Study section] User Study section, condition descriptions: the manuscript contrasts PLC against 'slowing down, stopping, and reactive collision avoidance' without stating that all four motion patterns were matched on velocity profile, acceleration limits, path curvature, or total travel time. This directly engages the stress-test concern; any mismatch would allow the rating gains to be explained by incidental dynamics rather than the extended reaction distance, undermining the central claim.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'typical motion patterns found in the literature' would benefit from one or two explicit citations to prior work on slowing-down or stopping behaviors to anchor the comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on the user study. The comments highlight important points for improving the clarity and verifiability of our empirical results. We address each major comment below and will revise the manuscript to incorporate the requested details.

read point-by-point responses

  1. Referee: [User Study section] User Study section: the abstract and results paragraph report 'significant improvement' in the three objectives for the Frontal Approach but supply no information on the statistical tests used, power analysis, counterbalancing of conditions, or exact participant motion parameters. These omissions are load-bearing because they prevent independent verification that the observed rating differences are attributable to the eight-meter PLC reaction distance rather than study design artifacts.

    Authors: We agree the abstract and results paragraph omit these details. The full manuscript describes a within-subjects design with 42 participants and reports statistically significant differences, but does not explicitly list the tests, power analysis, counterbalancing procedure, or motion parameters in those sections. We will expand the User Study section (and update the abstract if space permits) to include the statistical tests (e.g., specific non-parametric tests with p-values and effect sizes), power analysis, counterbalancing method, and exact motion parameters. This revision will enable independent verification that the gains are attributable to the extended reaction distance. revision: yes

  2. Referee: [User Study section] User Study section, condition descriptions: the manuscript contrasts PLC against 'slowing down, stopping, and reactive collision avoidance' without stating that all four motion patterns were matched on velocity profile, acceleration limits, path curvature, or total travel time. This directly engages the stress-test concern; any mismatch would allow the rating gains to be explained by incidental dynamics rather than the extended reaction distance, undermining the central claim.

    Authors: We acknowledge that the manuscript does not explicitly state matching across the four patterns on velocity profile, acceleration limits, path curvature, or travel time. Our implementation aimed to keep average velocity and acceleration comparable while varying only the reaction distance and lateral shift for PLC; however, this matching was not documented. We will revise the condition descriptions to explicitly report the matching criteria and provide the specific parameters (velocity, acceleration bounds, curvature, and travel times) used for each pattern, thereby isolating the effect of the eight-meter proactive behavior. revision: yes

Circularity Check

0 steps flagged

Empirical user study contains no derivation chain or fitted predictions.

full rationale

The paper describes an empirical user study (42 participants) comparing three service objectives across robot motion patterns in hallway scenarios. The PLC pattern is introduced descriptively as a lateral position change at eight meters; results are reported as statistical improvements in ratings versus baselines. No equations, parameter fits, model predictions, or self-citation chains appear in the provided text. All load-bearing claims rest on experimental data rather than any reduction of outputs to inputs by construction, satisfying the self-contained criterion for score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an empirical robotics study; it introduces no free parameters, mathematical axioms, or new invented entities.

pith-pipeline@v0.9.1-grok · 5830 in / 1150 out tokens · 31359 ms · 2026-06-29T16:57:50.130842+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

59 extracted references · 25 canonical work pages

  1. [1]

    URL https://spacecraft.ssl.umd.edu/ design lib/SSP50005rC.ISS crew integ.pdf

    National Aeronautics and Texas Space Administration Space Station Program Office Johnson Space Cen- ter Houston, 1999. URL https://spacecraft.ssl.umd.edu/ design lib/SSP50005rC.ISS crew integ.pdf. Accessed: 2024-09-13

    1999

  2. [2]

    On the use of post- hoc tests in environmental and biological sciences: A critical review.Heliyon, 10(3):e25131, 2024

    Codjo Emile Agbangba, Edmond Sacla Aide, Hermann Honfo, and Romain Gl `el`e Kakai. On the use of post- hoc tests in environmental and biological sciences: A critical review.Heliyon, 10(3):e25131, 2024. ISSN 2405-8440. doi: https://doi.org/10.1016/j.heliyon.2024. e25131. URL https://www.sciencedirect.com/science/ article/pii/S2405844024011629

    work page doi:10.1016/j.heliyon.2024 2024

  3. [3]

    Neziha Akalin, Annica Kristoffersson, and Amy Loutfi. Do you feel safe with your robot? factors influencing perceived safety in human-robot interaction based on subjective and objective measures.International Journal of Human-Computer Studies, 158:102744, 2022. ISSN 1071-5819. doi: https://doi.org/10.1016/j.ijhcs.2021. 102744. URL https://www.sciencedirec...

    work page doi:10.1016/j.ijhcs.2021 2022

  4. [4]

    URL https://www.woven-city

    Toyota Woven City, 2024. URL https://www.woven-city. global/. Accessed: 2024-09-01

    2024

  5. [5]

    Openmmlab pose estimation toolbox and benchmark

    MMPose Contributors. Openmmlab pose estimation toolbox and benchmark. https://github.com/open-mmlab/ mmpose, 2020. Accessed: 2024-09-15

    2020

  6. [6]

    Influence of emotional motions in human-robot interactions

    Magda Dubois, Josep-Arnau Claret, Luis Basa ˜nez, and Gentiane Venture. Influence of emotional motions in human-robot interactions. In Dana Kuli ´c, Yoshihiko Nakamura, Oussama Khatib, and Gentiane Venture, ed- itors,2016 International Symposium on Experimental Robotics, pages 799–808, Cham, 2017. Springer Inter- national Publishing

    2016

  7. [7]

    Field trial of an autonomous shopworker robot that aims to provide friendly encour- agement and exert social pressure

    Sachi Edirisinghe, Satoru Satake, Drazen Brscic, Yuyi Liu, and Takayuki Kanda. Field trial of an autonomous shopworker robot that aims to provide friendly encour- agement and exert social pressure. InProceedings of the 2024 ACM/IEEE International Conference on Human- Robot Interaction, pages 194–202, 2024

    2024

  8. [8]

    De- signing a robot guide for blind people in indoor environ- ments

    Catherine Feng, Shiri Azenkot, and Maya Cakmak. De- signing a robot guide for blind people in indoor environ- ments. InProceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, HRI’15 Extended Abstracts, page 107–108, New York, NY , USA, 2015. Association for Computing Machinery. ISBN 9781450333184. d...

    work page doi:10.1145/2701973.2702060 2015

  9. [9]

    D. Fox, W. Burgard, and S. Thrun. The dynamic window approach to collision avoidance.IEEE Robotics & Automation Magazine, 4(1):23–33, 1997. doi: 10.1109/ 100.580977

    1997

  10. [10]

    Hart, Jonathan P

    Anthony Francis, Claudia P ´erez-D’Arpino, Chengshu Li, Fei Xia, Alexandre Alahi, Rachid Alami, Aniket Bera, Abhijat Biswas, Joydeep Biswas, Rohan Chandra, Hao-Tien Lewis Chiang, Michael Everett, Sehoon Ha, Justin W. Hart, Jonathan P. How, Haresh Karnan, Tsang- Wei Edward Lee, Luis J. Manso, Reuth Mirksy, Soeren Pirk, Phani Teja Singamaneni, Peter Stone, ...

    work page arXiv 2023

  11. [11]

    Using the driving behavior of an automated vehicle to communicate intentions - a wizard of oz study

    Tanja Fuest, Lars Michalowski, Luca Tr ¨aris, Hanna Bellem, and Klaus Bengler. Using the driving behavior of an automated vehicle to communicate intentions - a wizard of oz study. In2018 21st International Confer- ence on Intelligent Transportation Systems (ITSC), pages 3596–3601, 2018. doi: 10.1109/ITSC.2018.8569486

    work page doi:10.1109/itsc.2018.8569486 2018

  12. [12]

    Legible and proac- tive robot planning for prosocial human-robot interac- tions.arXiv preprint arXiv:2404.03734, 2024

    Jasper Geldenbott and Karen Leung. Legible and proac- tive robot planning for prosocial human-robot interac- tions.arXiv preprint arXiv:2404.03734, 2024

    work page arXiv 2024

  13. [13]

    What makes human so different? analysis of human-humanoid robot interac- tion with a super wizard of oz platform

    Guillaume Gibert, Maxime Petit, Florian Lance, Gr ´egoire Pointeau, and Peter Ford Dominey. What makes human so different? analysis of human-humanoid robot interac- tion with a super wizard of oz platform. InInternational Conference on Intelligent Robots and Systems, 2013

    2013

  14. [14]

    Calderita, Alejandro Hidalgo-Paniagua, and Juan P

    Silvia Guill ´en Ruiz, Luis V . Calderita, Alejandro Hidalgo-Paniagua, and Juan P. Bandera Rubio. Mea- suring smoothness as a factor for efficient and socially accepted robot motion.Sensors, 20(23), 2020. ISSN 1424-8220. doi: 10.3390/s20236822. URL https://www. mdpi.com/1424-8220/20/23/6822

    work page doi:10.3390/s20236822 2020

  15. [15]

    LiDAR-based detection, tracking, and property estimation: A contemporary review

    Mahmudul Hasan, Junichi Hanawa, Riku Goto, Ryota Suzuki, Hisato Fukuda, Yoshinori Kuno, and Yoshi- nori Kobayashi. LiDAR-based detection, tracking, and property estimation: A contemporary review. 506:393–

  16. [16]

    doi: 10.1016/j.neucom.2022

    ISSN 09252312. doi: 10.1016/j.neucom.2022. 07.087. URL https://linkinghub.elsevier.com/retrieve/pii/ S0925231222009365

    work page doi:10.1016/j.neucom.2022 2022

  17. [17]

    Social force model for pedestrian dynamics.Physical review E, 51(5):4282, 1995

    Dirk Helbing and Peter Molnar. Social force model for pedestrian dynamics.Physical review E, 51(5):4282, 1995

    1995

  18. [18]

    Technical methods for social robots in museum settings: An overview of the literature

    Mehdi Hellou, JongYoon Lim, Norina Gasteiger, Minsu Jang, and Ho Seok Ahn. Technical methods for social robots in museum settings: An overview of the literature. International Journal of Social Robotics, 14(8):1767– 1786, 2022

    2022

  19. [19]

    Learning to navigate through crowded envi- ronments

    Peter Henry, Christian V ollmer, Brian Ferris, and Di- eter Fox. Learning to navigate through crowded envi- ronments. In2010 IEEE international conference on robotics and automation, pages 981–986. IEEE, 2010

    2010

  20. [20]

    Coarse-to-

    Dan Jia, Mats Steinweg, Alexander Hermans, and Bas- tian Leibe. Self-supervised person detection in 2d range data using a calibrated camera. In2021 IEEE International Conference on Robotics and Automation (ICRA), page 13301–13307. IEEE Press, 2021. doi: 10.1109/ICRA48506.2021.9561699. URL https://doi. org/10.1109/ICRA48506.2021.9561699

    work page doi:10.1109/icra48506.2021.9561699 2021

  21. [21]

    Likert scale: Explored and explained.British Journal of Applied Science & Technology, 7:396–403, 01 2015

    Ankur Joshi, Saket Kale, Satish Chandel, and Dinesh Pal. Likert scale: Explored and explained.British Journal of Applied Science & Technology, 7:396–403, 01 2015. doi: 10.9734/BJAST/2015/14975

    work page doi:10.9734/bjast/2015/14975 2015

  22. [22]

    A preliminary study of interactive navigation framework with situation-adaptive multimodal induce- ment: Pass-by scenario.International Journal of Social Robotics, 12:567–588, 2020

    Mitsuhiro Kamezaki, Ayano Kobayashi, Yuta Yokoyama, Hayato Yanagawa, Moondeep Shrestha, and Shigeki Sugano. A preliminary study of interactive navigation framework with situation-adaptive multimodal induce- ment: Pass-by scenario.International Journal of Social Robotics, 12:567–588, 2020

    2020

  23. [23]

    URL https://www.datanovia.com/en/lessons/ repeated-measures-anova-in-r/

    Alboukadel Kassambara, 2019. URL https://www.datanovia.com/en/lessons/ repeated-measures-anova-in-r/. Accessed: 2024-09- 11

    2019

  24. [24]

    Guiding preferred driving style using voice in au- tonomous vehicles: An on-road wizard-of-oz study

    Keunwoo Kim, Minjung Park, and Youn-kyung Lim. Guiding preferred driving style using voice in au- tonomous vehicles: An on-road wizard-of-oz study. In Proceedings of the 2021 ACM Designing Interactive Systems Conference, DIS ’21, page 352–364, New York, NY , USA, 2021. Association for Computing Machinery. ISBN 9781450384766. doi: 10.1145/3461778.3462056. ...

    work page doi:10.1145/3461778.3462056 2021

  25. [25]

    Phd thesis, Carnegie Mellon University, June 2010

    Rachel Kirby.Social robot navigation. Phd thesis, Carnegie Mellon University, June 2010. Available at https://www.ri.cmu.edu/pub files/2010/5/rk thesis.pdf

    2010

  26. [26]

    Com- panion: A constraint-optimizing method for person- acceptable navigation

    Rachel Kirby, Reid Simmons, and Jodi Forlizzi. Com- panion: A constraint-optimizing method for person- acceptable navigation. InRO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, pages 607–612, 2009. doi: 10.1109/ROMAN.2009.5326271

    work page doi:10.1109/roman.2009.5326271 2009

  27. [27]

    Segment anything

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015–4026, 2023

    2023

  28. [28]

    Physiological data-based evaluation of a social robot navigation system

    Hasan Kivrak, Pinar Uluer, Hatice Kose, Elif Gumuslu, Duygun Erol Barkana, Furkan Cakmak, and Sirma Yavuz. Physiological data-based evaluation of a social robot navigation system. In2020 29th IEEE Inter- national Conference on Robot and Human Interactive Communication (RO-MAN), pages 994–999, 2020. doi: 10.1109/RO-MAN47096.2020.9223539

    work page doi:10.1109/ro-man47096.2020.9223539 2020

  29. [29]

    Socially compliant mobile robot navigation via inverse reinforcement learning.The In- ternational Journal of Robotics Research, 35(11):1289– 1307, 2016

    Henrik Kretzschmar, Markus Spies, Christoph Sprunk, and Wolfram Burgard. Socially compliant mobile robot navigation via inverse reinforcement learning.The In- ternational Journal of Robotics Research, 35(11):1289– 1307, 2016

    2016

  30. [30]

    Po- liteness in human–robot interaction: A multi-experiment study with non-humanoid robots.International Jour- nal of Social Robotics, 14, 08 2022

    Shikhar Kumar, Eliran Itzhak, Yael Edan, Galit Nimrod, Vardit Sarne-Fleischmann, and Noam Tractinsky. Po- liteness in human–robot interaction: A multi-experiment study with non-humanoid robots.International Jour- nal of Social Robotics, 14, 08 2022. doi: 10.1007/ s12369-022-00911-z

    2022

  31. [31]

    A wizard-of-oz study of curiosity in human-robot interaction

    Edith Law, Vicky Cai, Qi Feng Liu, Sajin Sasy, Joslin Goh, Alex Blidaru, and Dana Kuli ´c. A wizard-of-oz study of curiosity in human-robot interaction. In2017 26th IEEE International Symposium on Robot and Hu- man Interactive Communication (RO-MAN), pages 607– 614, 2017. doi: 10.1109/ROMAN.2017.8172365

    work page doi:10.1109/roman.2017.8172365 2017

  32. [32]

    The effect of politeness strategy on human- robot collaborative interaction on malfunction of robot vacuum cleaner

    Yeoreum Lee, Jae-eul Bae, Sona S Kwak, and Myung- Suk Kim. The effect of politeness strategy on human- robot collaborative interaction on malfunction of robot vacuum cleaner. InRSS workshop on HRI, 2011

    2011

  33. [33]

    Benedikt Leichtmann and Verena Nitsch. How much distance do humans keep toward robots? literature re- view, meta-analysis, and theoretical considerations on personal space in human-robot interaction.Journal of Environmental Psychology, 68:101386, 2020. ISSN 0272-4944. doi: https://doi.org/10.1016/j.jenvp.2019. 101386. URL https://www.sciencedirect.com/sci...

    work page doi:10.1016/j.jenvp.2019 2020

  34. [34]

    The marathon 2: A navigation system

    Steve Macenski, Francisco Mart ´ın, Ruffin White, and Jonatan Gin ´es Clavero. The marathon 2: A navigation system. In2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2718–2725. IEEE, 2020

    2020

  35. [35]

    Relationships between ignoring instructions and response bias when completing ques- tionnaires.The Japanese Journal of Psychology, 87(4): 354–363, 2016

    Shinya Masuda, Takayuki Sakagami, Kazuyo Kitaoka, and Megumi Sasaki. Relationships between ignoring instructions and response bias when completing ques- tionnaires.The Japanese Journal of Psychology, 87(4): 354–363, 2016. (in Japanese)

    2016

  36. [36]

    Core challenges of social robot navigation: A survey.ACM Transactions on Human-Robot Interac- tion, 12(3):1–39, 2023

    Christoforos Mavrogiannis, Francesca Baldini, Allan Wang, Dapeng Zhao, Pete Trautman, Aaron Steinfeld, and Jean Oh. Core challenges of social robot navigation: A survey.ACM Transactions on Human-Robot Interac- tion, 12(3):1–39, 2023

    2023

  37. [37]

    A wizard of oz study on passengers’ experiences of a robo-taxi service in real-life settings

    Johanna Meurer, Christina Pakusch, Gunnar Stevens, Dave Randall, and V olker Wulf. A wizard of oz study on passengers’ experiences of a robo-taxi service in real-life settings. InProceedings of the 2020 ACM Designing Interactive Systems Conference, DIS ’20, page 1365–1377, New York, NY , USA, 2020. Association for Computing Machinery. ISBN 9781450369749. ...

    work page doi:10.1145/3357236.3395465 2020

  38. [38]

    URL https://laws.e-gov.go.jp/law/ 325CO0000000338/20230526 504CO0000000393# Mp-Ch 5-Se 2

    Transport Ministry of Land, Infrastructure and Tourism, 2023. URL https://laws.e-gov.go.jp/law/ 325CO0000000338/20230526 504CO0000000393# Mp-Ch 5-Se 2. Accessed: 2024-09-13

    2023

  39. [39]

    Understanding driver-automated vehicle interactions through wizard of oz design improvisation

    Brian Ka-Jun Mok, David Sirkin, Srinath Sibi, David Bryan Miller, and Wendy Ju. Understanding driver-automated vehicle interactions through wizard of oz design improvisation. volume 8, pages 380–

  40. [40]

    doi: 10.17077/ drivingassessment.1598

    University of Iowa, 6 2015. doi: 10.17077/ drivingassessment.1598. URL https://pubs.lib.uiowa.edu/ driving/article/id/28548/

    2015

  41. [41]

    M. A. Viraj J. Muthugala, Ayyalusami Vengadesh, Xinke Wu, Mohan Rajesh Elara, Masami Iwase, Lingyun Sun, and Jiang Hao. Expressing attention requirement of a floor cleaning robot through interactive lights.Au- tomation in Construction, 110:103015, 2020. ISSN 0926-5805. doi: https://doi.org/10.1016/j.autcon.2019. 103015. URL https://www.sciencedirect.com/s...

    work page doi:10.1016/j.autcon.2019 2020

  42. [42]

    Trajectory mod- ification considering dynamic constraints of autonomous robots

    Christoph Roesmann, Wendelin Feiten, Thomas Woesch, Frank Hoffmann, and Torsten Bertram. Trajectory mod- ification considering dynamic constraints of autonomous robots. InROBOTIK 2012; 7th German Conference on Robotics, pages 1–6, 2012

    2012

  43. [43]

    How cognitive heuristics can explain social interactions in spatial movement.Journal of the Royal Society Interface, 13(121):20160439, 2016

    Michael J Seitz, Nikolai WF Bode, and Gerta K ¨oster. How cognitive heuristics can explain social interactions in spatial movement.Journal of the Royal Society Interface, 13(121):20160439, 2016

    2016

  44. [44]

    Vint: A foundation model for visual navigation.arXiv preprint arXiv:2306.14846, 2023

    Dhruv Shah, Ajay Sridhar, Nitish Dashora, Kyle Sta- chowicz, Kevin Black, Noriaki Hirose, and Sergey Levine. ViNT: A foundation model for visual navigation. In7th Annual Conference on Robot Learning, 2023. URL https://arxiv.org/abs/2306.14846

    work page arXiv 2023

  45. [45]

    Behavior of delivery robot in human-robot collaborative spaces during navigation

    Kiran Jot Singh, Divneet Singh Kapoor, Mohamed Abouhawwash, Jehad F Al-Amri, Shubham Mahajan, and Amit Kant Pandit. Behavior of delivery robot in human-robot collaborative spaces during navigation. Intelligent Automation & Soft Computing, 35(1), 2023

    2023

  46. [46]

    Video prototyping in human-robot interaction: results from a qualitative study

    Dag Sverre Syrdal, Nuno Otero, and Kerstin Dautenhahn. Video prototyping in human-robot interaction: results from a qualitative study. InProceedings of the 15th European Conference on Cognitive Ergonomics: The Ergonomics of Cool Interaction, ECCE ’08, New York, NY , USA, 2008. Association for Computing Machinery. ISBN 9781605583990. doi: 10.1145/1473018.1...

    work page doi:10.1145/1473018.1473055 2008

  47. [47]

    Wizard of oz vs autonomous: Chil- dren’s perception changes according to robot’s operation condition

    Daniel Tozadore, Adam Pinto, Roseli Romero, and Gabriele Trovato. Wizard of oz vs autonomous: Chil- dren’s perception changes according to robot’s operation condition. In2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO- MAN), pages 664–669, 2017. doi: 10.1109/ROMAN. 2017.8172374

    work page doi:10.1109/roman 2017

  48. [48]

    Tsui, Munjal Desai, and Holly A

    Katherine M. Tsui, Munjal Desai, and Holly A. Yanco. Considering the bystander’s perspective for indirect human-robot interaction. In2010 5th ACM/IEEE Inter- national Conference on Human-Robot Interaction (HRI), pages 129–130, 2010. doi: 10.1109/HRI.2010.5453230

    work page doi:10.1109/hri.2010.5453230 2010

  49. [49]

    The double sphere camera model

    Vladyslav Usenko, Nikolaus Demmel, and Daniel Cre- mers. The double sphere camera model. In2018 International Conference on 3D Vision (3DV), pages 552–560. IEEE, 2018

    2018

  50. [50]

    Reciprocal n-body collision avoidance

    Jur Van Den Berg, Stephen J Guy, Ming Lin, and Dinesh Manocha. Reciprocal n-body collision avoidance. In Robotics Research: The 14th International Symposium ISRR, pages 3–19. Springer, 2011

    2011

  51. [51]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. V on Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL h...

    2017

  52. [52]

    Michael Walters, Kerstin Dautenhahn, Kheng Koay, Christina Kaouri, Rene Boekhorst, Chrystopher Nehaniv, Iain Werry, and D. Lee. Close encounters: spatial distances between people and a robot of mechanistic appearance. pages 450 – 455, 02 2005. ISBN 0-7803- 9320-1. doi: 10.1109/ICHR.2005.1573608

    work page doi:10.1109/ichr.2005.1573608 2005

  53. [53]

    Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

    Chien-Yao Wang, Alexey Bochkovskiy, and Hong- Yuan Mark Liao. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7464– 7475, June 2023

    2023

  54. [54]

    Qureshi, Anthony Simeonov, Mayur J

    Grady Williams, Paul Drews, Brian Goldfain, James M. Rehg, and Evangelos A. Theodorou. Aggressive driving with model predictive path integral control. In2016 IEEE International Conference on Robotics and Automation (ICRA), pages 1433–1440, 2016. doi: 10.1109/ICRA. 2016.7487277

    work page doi:10.1109/icra 2016

  55. [55]

    Learning model predictive controllers with real-time attention for real-world navigation

    Xuesu Xiao, Tingnan Zhang, Krzysztof Marcin Choro- manski, Tsang-Wei Edward Lee, Anthony Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, and Vikas Sindhwani. Learning model predictive controllers with real-time attention for real-world navigat...

    2023

  56. [56]

    Robotic delivery worker in the dark: Assessment of perceived safety from sidewalk autonomous delivery robots’ lighting colors.Applied Sciences, 14(14), 2024

    Fan Xu, Duanduan Liu, Chao Zhou, and Jing Hu. Robotic delivery worker in the dark: Assessment of perceived safety from sidewalk autonomous delivery robots’ lighting colors.Applied Sciences, 14(14), 2024. ISSN 2076-3417. doi: 10.3390/app14145983. URL https://www.mdpi.com/2076-3417/14/14/5983

    work page doi:10.3390/app14145983 2024

  57. [57]

    Zamfirescu-Pereira, David Sirkin, David Goedicke, Ray LC, Natalie Friedman, Ilan Mandel, Nikolas Marte- laro, and Wendy Ju

    J.D. Zamfirescu-Pereira, David Sirkin, David Goedicke, Ray LC, Natalie Friedman, Ilan Mandel, Nikolas Marte- laro, and Wendy Ju. Fake it to make it: Exploratory prototyping in hri. InCompanion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’21 Companion, page 19–28, New York, NY , USA,

    2021

  58. [58]

    ISBN 9781450382908

    Association for Computing Machinery. ISBN 9781450382908. doi: 10.1145/3434074.3446909. URL https://doi.org/10.1145/3434074.3446909

    work page doi:10.1145/3434074.3446909

  59. [59]

    Planning-based prediction for pedestrians

    Brian D Ziebart, Nathan Ratliff, Garratt Gallagher, Christoph Mertz, Kevin Peterson, J Andrew Bagnell, Martial Hebert, Anind K Dey, and Siddhartha Srini- vasa. Planning-based prediction for pedestrians. In2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3931–3936. IEEE, 2009

    2009