Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

HuNavSim 2.0: An Enhanced Human Navigation Simulator for Human-Aware Robot Navigation

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read HuNavSim 2.0 claims that adding controlled random variation to Social Force Model parameters—and scripting human actions with Behavior Trees—produces variable, more realistic human navigation trajectories, and bundles this with wrappers…

desk verdict Useful open-source engineering update; the realism claim outruns the evidence. read the letter →

arxiv 2507.17317 v2 pith:3S67ONVW submitted 2025-07-23 cs.RO

Miguel Escudero-Jiménez , Noé Pérez-Higueras , Andrés Martínez-Silva , Fernando Caballero , Luis Merino This is my paper · ORCID
classification cs.RO
keywords socialforcemodelbehaviortreeshuman-awarenavigationpedestriansimulationrobotbenchmarkingROS2metricsvariability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

HuNavSim 2.0 tries to establish that a human-navigation simulator can produce believable, varied pedestrian behavior rather than the uniform, deterministic motion typical of crowd models. It does this by injecting controlled random variation into the parameters of the Social Force Model and by giving users a library of Behavior Tree actions and conditions to script complex human activities such as forming a conversation, inspecting a robot, or following a coworker. The paper argues that this combination, together with wrappers for several robot simulators and a 32-metric evaluation suite, makes the tool useful for developing and benchmarking human-aware robot navigation. A sympathetic reader would see the contribution as an engineering platform whose central bet is that parameter noise plus composable behaviors is enough to bridge the gap between scripted agents and real pedestrian unpredictability.

What carries the argument

The load-bearing mechanism is the Social Force Model with parameter noise, where each agent's motion is driven by forces toward goals, away from obstacles, and between agents, and where force parameters are sampled from normal distributions ($\mathcal{N}(\mu,\sigma^2)$) in feasible ranges to vary behavior across agents and runs. The second mechanism is a Behavior Tree interpreter: a tree of action nodes (GoTo, FollowAgent, ApproachRobot, ConversationFormation, SaySomething) and condition nodes (isAtPosition, IsSpeaking, IsLookingAtMe) that determines which behavior an agent executes and when to switch. The parameter sensitivity analysis is what guarantees the noise stays small enough to be realistic while large enough to be visible. The whole system is organized as a manager that receives agent states from a robot simulator wrapper, computes next states from the active behavior trees, and sends them back, with an evaluator module that logs data and computes metrics.

What would settle it

Run the same scenario twice in HuNavSim 2.0, once with default deterministic parameters and once with stochastic parameter sampling, and measure the distribution of trajectories (for example, path curvature, speed profiles, and clearance distances) against a public dataset of recorded pedestrian crossings and overtakes; if the stochastic trajectories are not statistically closer to or no more variable than the deterministic ones, the realism claim is falsified.

Watch

Extended reading notes

Core claim

The central discovery is an architecture-level claim: behavioral variability in simulated humans can be produced by treating the Social Force Model's parameters (such as desired speed, relaxation time, and force strengths) as random variables sampled from normal distributions over precomputed feasible ranges. With this change, repeated runs of the same scenario no longer produce identical agent trajectories; instead they produce a family of plausible trajectories. On top of this, the paper adds a set of Behavior Tree nodes that encode social conditions (e.g., is someone speaking, is another agent looking at me) and actions (approach robot, follow agent, form conversation, say something), so that whole interaction sequences can be authored as trees and executed by the HuNavSim manager. The tool's contribution is therefore twofold: a local navigation layer with tunable stochasticity and a global behavior layer with composable social scripts, both exposed through a scenario editor and measured by an extensible metric suite.

Load-bearing premise

The central claim stands or falls on the assumption that sampling the Social Force Model's parameters from bell-curve distributions—within ranges the paper does not reveal—produces trajectories closer to real human walking, since the paper provides no comparison with recorded human motion.

Editorial extensions

If this is right

  • If the noise injection works as claimed, every stochastic run of a scenario is a different test case, so navigation policies evaluated in HuNavSim 2.0 face a distribution of human behaviors instead of one scripted response.
  • Behavior Tree actions and conditions let scenario authors compose social episodes—group conversations, robot inspection, follow-me—so benchmarks can cover interaction-rich situations that plain point-to-point crowd motion cannot represent.
  • Because the tool wraps several robot simulators, the same behavior trees, scenario files, and metric definitions can be reused across simulators, making cross-simulator comparisons more direct.
  • The 32-metric evaluation suite, including the four danger-and-surprise metrics, gives users a common yardstick to compare social navigation approaches without committing to one fixed benchmark.
  • The interactive scenario editor turns scenario creation into a visual process, shortening the time between designing a social situation and running it in simulation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not compare noisy trajectories against real pedestrian data, so its realism claim is untested; a natural extension would be fitting the noise variances to recorded pedestrian trajectories and reporting the divergence reduction before and after.
  • The single sensitivity analysis that sets the feasible ranges may not transfer to other densities or environments; the tool's own scenario editor makes this easy to check by running the same behavior tree at different crowd densities and measuring trajectory variability.
  • Because behavior trees encode social scripts separately from the noisy motion layer, users could also author proactive social behaviors that the current example scenarios only begin to explore, such as queuing, helping, or gesturing.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper presents HuNavSim 2.0, an open-source ROS 2-based human navigation simulator for developing and evaluating human-aware robot navigation systems. The new version adds (i) a library of Behavior Tree actions and conditions intended to produce complex and realistic human behaviors, (ii) stochastic noise injection into Social Force Model parameters to introduce trajectory variability, (iii) wrappers for Gazebo Classic, Gazebo Fortress, Nvidia Isaac Sim, and Webots, and (iv) a scenario-creation workflow using RViz 2 panels. The tool also extends the HuNavSim metric suite with additional evaluation metrics. The paper describes these features and gives a qualitative warehouse-worker example, but it contains no quantitative evaluation of the claimed realism improvements or of the tool's behavior-generation capabilities.

Significance. If the claims are substantiated, HuNavSim 2.0 would be a useful community resource: it is open-source, integrates with several widely used robot simulators, provides a Behavior Tree node library, includes a flexible metric system, and ships Docker-based deployment and interactive scenario tools. The multi-simulator wrappers and the RViz2-based scenario editor are practical strengths that lower the entry barrier for human-aware navigation research. However, the two central claims — that parameter noise yields 'variable and more realistic human navigation' and that the Behavior Tree actions yield 'complex and realistic human behaviors' — are presented without any quantitative or comparative evidence. The paper is therefore better evaluated as a system description than as a validated contribution to simulation realism.

major comments (2)
  1. [Section IV-A] The central claim of contribution (ii) — that adding controlled noise to Social Force Model parameters produces 'variable and more realistic human navigation' — is not supported by any reported evidence. The paragraph states that 'We performed a parameter sensitivity analysis to identify feasible ranges that produce small yet realistic variations', but neither the parameters perturbed, the identified ranges, nor the sensitivity results are given. The sampling scheme is also ambiguous: it is not stated whether parameter values are drawn once per agent, once per simulation run, or at every integration step. These regimes have very different consequences: per-agent sampling yields a heterogeneous but deterministic population, whereas per-step sampling turns the SFM into a stochastic differential equation that may produce high-frequency jitter rather than smooth human-like variability. Finally, there is no comparison against real pedestrian trajectory data (e.g., ETH/UCY) or against the deterministic SFM baseline. The realism claim is therefore not independently checkable and needs to be backed by quantitative evidence, including a specification of the noise model and an analysis of the resulting trajectory distributions.
  2. [Sections IV-B and VI] The abstract and conclusions state that the Behavior Tree actions and conditions 'compound complex and realistic human behaviors', but the only supporting evidence is a narrative description of a warehouse scenario involving two workers. No evaluation is provided to show that the BT-enabled behaviors are realistic or that they go beyond what the SFM alone can generate. Since 'realistic' is a stated contribution in both the introduction and the conclusion, the paper should include at least a qualitative validation (e.g., a user study, comparison with real human behavior data, or an analysis of behavior variability) to justify this claim. Without such evidence, the BT contribution rests on assertion rather than demonstration.
minor comments (5)
  1. [Section V-B] There is a numerical inconsistency in the metric count: the text says 'adding two new interesting metrics', then refers to 'The new four metrics', and then states 'That gives a total of 32 metrics' when the base is said to be 28. Since 28 + 4 = 32, the 'two' is likely a typo, but the contradiction should be corrected for clarity.
  2. [Title and header] The title in the full text reads 'A Enhanced Human Navigation Simulator' and should be 'An Enhanced Human Navigation Simulator'.
  3. [Throughout] Several typesetting errors occur: 'InHuNavSim' and 'HuNavSim2.0' are missing spaces in the introduction and conclusion; 'Warehoure' in the Fig. 4 caption should be 'Warehouse'; 'botton' should be 'bottom' in the same caption.
  4. [Section V-B] The paper claims the metric set is 'the most comprehensive collection of metrics for human-aware navigation to date' but does not compare the metric list with those of SocNavBench, CrowdBot, or other recent benchmarks beyond citing them. A succinct comparison table would make this claim verifiable.
  5. [Section IV-B] The example Behavior Tree for Worker 2 is described in detail in the text, but the corresponding figure (Fig. 3) is not discussed step-by-step; referring to node names in the figure would help readers connect the narrative to the tree structure.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: HuNavSim 2.0 is a tool presentation; the Section IV-A realism claim is under-validated, not circular.

full rationale

This is a software-tool paper with no derivation chain, fitted predictions, or uniqueness arguments. Section IV-A states that noise added to Social Force Model parameters produces "variable and more realistic human navigation" and that a parameter sensitivity analysis identified "feasible ranges that produce small yet realistic variations," but the ranges, sampled parameters, and validation data are not reported. This is an unsupported and under-specified correctness/evidence claim, not circularity: the paper does not define "realistic" in terms of the noise distribution, nor does it fit a parameter to data and then rename the fit as a prediction. There are no equations equating outputs to inputs by construction. Self-citations to the authors' prior HuNavSim paper [1] for the core architecture and the 28-metric list are normal continuation references; they are not used as an external uniqueness theorem or to forbid alternative conclusions, so they are not load-bearing circularity. The Section V-B inconsistency between "adding two new metrics" and "new four metrics" with a total of 32 is an internal reporting error, not a circular step. The paper would be strengthened by external benchmarking against real pedestrian trajectories, but that absence is a validation gap, not circular reasoning.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new entities or mathematical claims. Its load-bearing assumptions are that SFM plus noise mimics human variability and that the BT node set suffices for realistic behavior; both are unvalidated in the text.

free parameters (1)
  • Noise distribution parameters for Social Force Model factors = Not disclosed in paper
    The central claim of trajectory variability relies on normal-distribution ranges from an unreported sensitivity analysis; these are free parameters set by the authors.
assumptions (3)
  • domain assumption The Social Force Model is an adequate model of human navigation behavior.
    Section IV-A states HuNavSim employs SFM as the underlying movement model; realism claims inherit SFM's validity.
  • ad hoc to paper Adding controlled noise to SFM parameters produces trajectories closer to real human navigation than deterministic SFM.
    Section IV-A asserts this without data; this is the load-bearing premise for the enriched human local navigation contribution.
  • ad hoc to paper The set of Behavior Tree actions and conditions, combined via BTs, yields complex and realistic human social behaviors.
    Section IV-B describes the node set and warehouse example, but no evaluation shows the resulting behaviors are realistic.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HuNavSim 2.0: An Enhanced Human Navigation Simulator for Human-Aware Robot Navigation." pith.science (2026). https://pith.science/paper/3S67ONVW

@misc{pith2026250717317,
  author       = {Pith},
  title        = {Pith review of: HuNavSim 2.0: An Enhanced Human Navigation Simulator for Human-Aware Robot Navigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3S67ONVW}},
  note         = {Machine review of arXiv:2507.17317}
}
read the original abstract

This work presents a new iteration of the Human Navigation Simulator (HuNavSim), a novel open-source tool for the simulation of different human-agent navigation behaviors in scenarios with mobile robots. The tool, programmed under the ROS 2 framework, can be used together with different well-known robotics simulators such as Gazebo or NVidia Isaac Sim. The main goal is to facilitate the development and evaluation of human-aware robot navigation systems in simulation. In this new version, several features have been improved and new ones added, such as the extended set of actions and conditions that can be combined in Behavior Trees to compound complex and realistic human behaviors.

Figures

Figures reproduced from arXiv: 2507.17317 by the authors.

Figure 1
Figure 1. Capture of HuNav agents in the NVidia Isaac Sim [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Scheme of HuNavSim with Robotics simulators [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Example Behavior Tree of the Worker 2 IV. ENHANCED SOCIAL HUMAN BEHAVIOR A. Human local navigation model HuNavSim employs the Social Force Model [5] and its extension to groups [24], [25], as the underlying model to lead the movement of human agents. This model, and many others, generally apply the same behavior patterns to all agents, resulting in uniform responses across similar scenarios. Consequently, they fail … view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Examples of simulators and some scenarios provided. From left to right and up to botton: Warehoure and home in [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 6
Figure 6. Figure 6: Sequential agent dialog In short, the HuNav RViz 2 panel turns scenario creation into an interactive, visual task while preserving YAML/BT flexibility and real-time feedback. Further details and examples are available in the hunav_rviz2_panel repository. B. Evaluation …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Validating Virtual Reality for Studying Multimodal Human-Robot Interaction in Socially Aware Robot Navigation

    cs.RO 2026-07 conditional novelty 5.0 of 10

    Matched real-world vs VR co-navigation with a PR2 shows similar social-awareness ratings and consistent trajectory and head-orientation patterns, supporting VR for multimodal SRN studies.

Reference graph

Works this paper leans on

27 extracted references · 23 canonical work pages · cited by 1 Pith paper

  1. [1]

    Hunavsim: A ros 2 human navigation simulator for benchmarking human-aware robot navigation,

    N. P ´erez-Higueras, R. Otero, F. Caballero, and L. Merino, “Hunavsim: A ros 2 human navigation simulator for benchmarking human-aware robot navigation,”IEEE Robotics and Automation Letters, vol. 8, no. 11, pp. 7130–7137, 2023

    work page 2023

  2. [2]

    Principles and guidelines for evaluating social robot navigation algorithms,

    A. Francis, C. P ´erez-D’Arpino, C. Li, F. Xia, A. Alahi, R. Alami, A. Bera, A. Biswas, J. Biswas, R. Chandra, H.-T. L. Chiang, M. Everett, S. Ha, J. Hart, J. P. How, H. Karnan, T.-W. E. Lee, L. J. Manso, R. Mirsky, S. Pirk, P. T. Singamaneni, P. Stone, A. V . Taylor, P. Trautman, N. Tsoi, M. V ´azquez, X. Xiao, P. Xu, N. Yokoyama, A. Toshev, and R. Mart ...

    2025

  3. [3]

    A survey on socially aware robot navigation: Taxonomy and future challenges,

    P. T. Singamaneni, P. Bachiller-Burgos, L. J. Manso, A. Garrell, A. Sanfeliu, A. Spalanzani, and R. Alami, “A survey on socially aware robot navigation: Taxonomy and future challenges,”The International Journal of Robotics Research, vol. 43, no. 10, pp. 1533–1572, 2024. [Online]. Available: https://doi.org/10.1177/02783649241230562

    work page doi:10.1177/02783649241230562 2024

  4. [4]

    Colledanchise and P

    M. Colledanchise and P. ¨Ogren,Behavior Trees in Robotics and AI: An Introduction, ser. Chapman & Hall/CRC Artificial Intelligence and Robotics Series. Boca Raton, US: CRC Press, 2018

    work page 2018

  5. [5]

    Social force model for pedestrian dynamics

    Helbing and Moln ´ar, “Social force model for pedestrian dynamics.” Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics, vol. 51 5, pp. 4282–4286, 1995

    work page 1995

  6. [6]

    Mengeros: a crowd simulation tool for autonomous robot navigation,

    A. Aroor, S. L. Epstein, and R. Korpan, “Mengeros: a crowd simulation tool for autonomous robot navigation,” inAAAI 2017 Fall Symposium on Artificial Intelligence for Human-Robot Interaction, 2017, pp. 123–125

    work page 2017

  7. [7]

    Reciprocal n-body collision avoidance,

    J. van den Berg, S. J. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” inRobotics Research, C. Pradalier, R. Siegwart, and G. Hirzinger, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 3–19

    work page 2011

  8. [8]

    Pedestrian simulation using geometric reasoning in velocity space,

    S. Curtis and D. Manocha, “Pedestrian simulation using geometric reasoning in velocity space,” inPedestrian and Evacuation Dynamics 2012, U. Weidmann, U. Kirsch, and M. Schreckenberg, Eds. Cham: Springer International Publishing, 2012, pp. 875–890

    work page 2012

Show all 27 references
  1. [9]

    Simulating intelligent hu- man agents for intricate social robot navigation,

    A. Favier, P.-T. Singamaneni, and R. Alami, “Simulating intelligent hu- man agents for intricate social robot navigation,” inRSS 2021 Workshop on Social Robot Navigation, Washington, United States, Jul 2021

    2021

  2. [10]

    Imhus: Intelligent multi-human simulator,

    O. Hauterville, C. Fern ´andez, P.-T. Singamaneni, A. Favier, V . Matell´an, and R. Alami, “Imhus: Intelligent multi-human simulator,” inIEEE/RSJ IROS2022 Workshop: Artificial Intelligence for Social Robots Interacting with Humans in the Real World, Kyoto, Japan, Oct 2022. [On...

    2022

  3. [11]

    Hateb-2: Reactive planning and decision making in human-robot co-navigation,

    P. Teja S. and R. Alami, “Hateb-2: Reactive planning and decision making in human-robot co-navigation,” in2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO- MAN), 2020, pp. 179–186

    2020

  4. [12]

    Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd,

    F. Grzeskowiak, D. Gonon, D. Dugas, D. Paez-Granados, J. J. Chung, J. Nieto, R. Siegwart, A. Billard, M. Babel, and J. Pettr ´e, “Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd,” in2021 IEEE Int...

    2021

  5. [13]

    Sean 2.0: Formalizing and generating social situations for robot navigation,

    N. Tsoi, A. Xiang, P. Yu, S. S. Sohn, G. Schwartz, S. Ramesh, M. Hussein, A. W. Gupta, M. Kapadia, and M. V ´azquez, “Sean 2.0: Formalizing and generating social situations for robot navigation,”IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 047–11 054, 2022

    2022

  6. [14]

    Sean: Social environment for autonomous navigation,

    N. Tsoi, M. Hussein, J. Espinoza, X. Ruiz, and M. V ´azquez, “Sean: Social environment for autonomous navigation,” inthe 8th International Conference on Human-Agent Interaction (HAI ’20), 11 2020, pp. 281– 283

    2020

  7. [15]

    Socialgym: A framework for benchmarking social robot navigation,

    J. Holtz and J. Biswas, “Socialgym: A framework for benchmarking social robot navigation,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 11 246–11 252

    2022

  8. [16]

    Socialgym 2.0: Simulator for multi-agent social robot navigation in shared human spaces,

    Z. Sprague, R. Chandra, J. Holtz, and J. Biswas, “Socialgym 2.0: Simulator for multi-agent social robot navigation in shared human spaces,” 2023. [Online]. Available: https://arxiv.org/abs/2303.05584

    2023 arXiv

  9. [17]

    Demonstrating Arena 3.0: Advancing Social Navigation in Collaborative and Highly Dynamic Environments,

    L. K ¨astner, V . Shcherbyna, H. Zeng, T. A. Le, M. H.-K. Schreff, H. Osmaev, N. T. Tran, D. Diaz, J. Golebiowski, H. Soh, and J. Lam- brecht, “Demonstrating Arena 3.0: Advancing Social Navigation in Collaborative and Highly Dynamic Environments,” inProceedings of Robotics: Sc...

    2024

  10. [18]

    Arena 4.0: A comprehensive ros2 development and benchmarking platform for human-centric navigation using generative-model-based environment generation,

    V . Shcherbyna1, L. K ¨astner, D. Diaz, H. G. Nguyen, M. H.-K. Schreff, T. Lenz, J. Kreutz, A. Martban, H. Zeng, and H. Soh, “Arena 4.0: A comprehensive ros2 development and benchmarking platform for human-centric navigation using generative-model-based environment generation,...

    2024 arXiv

  11. [19]

    Socrates: Towards automated scenario-based testing of social navigation algorithms,

    S. R. Marpally, P. Goyal, and H. Soh, “Socrates: Towards automated scenario-based testing of social navigation algorithms,” 2024. [Online]. Available: https://arxiv.org/abs/2412.19595

    2024 arXiv

  12. [20]

    Conav: A benchmark for human-centered collaborative navigation,

    C. Li, X. Sun, P. Chen, J. Fan, Z. Wang, Y . Liu, J. Zhu, C. Gan, and M. Tan, “Conav: A benchmark for human-centered collaborative navigation,” 2024. [Online]. Available: https://arxiv.org/abs/2406.02425

    2024 arXiv

  13. [21]

    Benchmarking metric ground navigation,

    D. Perille, A. Truong, X. Xiao, and P. Stone, “Benchmarking metric ground navigation,” in2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Abu Dhabi, UAE, 2020, pp. 116– 121

    2020

  14. [22]

    Bench-mr: A motion planning benchmark for wheeled mobile robots,

    E. Heiden, L. Palmieri, L. Bruns, K. O. Arras, G. S. Sukhatme, and S. Koenig, “Bench-mr: A motion planning benchmark for wheeled mobile robots,”IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4536–4543, 2021. [Online]. Available: https: //ieeexplore.ieee.org/document/9387068

    2021

  15. [23]

    Socnavbench: A grounded simulation testing framework for evaluating social navigation,

    A. Biswas, A. Wang, G. Silvera, A. Steinfeld, and H. Admoni, “Socnavbench: A grounded simulation testing framework for evaluating social navigation,”ACM Transactions on Human-Robot Interaction, jul

  16. [24]

    Experimental study of the behavioural mechanisms underlying self-organization in human crowds,

    M. Moussa ¨ıd, D. Helbing, S. Garnier, A. Johansson, M. Combe, and G. Theraulaz, “Experimental study of the behavioural mechanisms underlying self-organization in human crowds,”Proceedings. Biological sciences, vol. 276, no. 1668, p. 2755—2762, August 2009

    2009

  17. [25]

    The walking behaviour of pedestrian social groups and its impact on crowd dynamics,

    M. Moussa ¨ıd, N. Perozo, S. Garnier, D. Helbing, and G. Theraulaz, “The walking behaviour of pedestrian social groups and its impact on crowd dynamics,”PLoS One, vol. 5, no. 4, 2010

    2010

  18. [26]

    Towards benchmarking human-aware social robot navigation: A new perspective and metrics,

    P. T. Singamaneni, A. Favier, and R. Alami, “Towards benchmarking human-aware social robot navigation: A new perspective and metrics,” in2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023, pp. 914–921

    2023

  19. [2022]

    Available: https://doi.org/10.1145/3476413

    [Online]. Available: https://doi.org/10.1145/3476413

    doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.