# NavWareSet: A Dataset of Socially Compliant and Non-Compliant Robot Navigation

Johnata Brayan, Sihao Deng, Armando Alves Neto, Iaroslav Okunevich,
Tomas Krajnik, Francois Bremond, Zhi Yan

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Johnata Brayan, Sihao Deng, Armando Alves Neto, Iaroslav Okunevich, Tomas Krajnik, et al. NavWareSet: A Dataset of Socially Compliant and Non-Compliant Robot Navigation. 2025. hal-05231729

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# NavWareSet: A Dataset of Socially Compliant and Non-Compliant Robot Navigation

Journal Title
XX(X):1–10
©The Author(s) 2016
Reprints and permission:
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DOI: 10.1177/ToBeAssigned
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SAGE

Johnata Brayan<fnref n="1" />,<fnref n="2" />, Sihao Deng<fnref n="3" />, Armando Alves Neto<fnref n="2" />, Iaroslav Okunevich<fnref n="1" />, Tomas Krajnik<fnref n="4" />, Francois Bremond<fnref n="5" />, Zhi Yan<fnref n="1" />,<fnref n="6" />

## Abstract

This paper presents NavWareSet, a novel dataset crafted to advance socially compliant robot navigation research. NavWareSet provides multi-modal recordings of both socially compliant and non-compliant robot trajectories in controlled indoor environments. Drawing upon seven carefully selected scenarios, it captures complex human-robot interactions and a range of navigation challenges that mirror realistic social contexts. NavWareSet establishes a rich benchmark for evaluating and training navigation algorithms by incorporating two distinct robot platforms – Toyota Human Support Robot (HSR) and Clearpath's Jackal – and systematically varying their navigation behaviors. With data modalities spanning lidar, RGB-D camera, odometry, and human position annotations, NavWareSet enables fine-grained analysis of the robot's decision-making process and its impact on human comfort and safety. Ultimately, this dataset provides a versatile resource for developing robust, ethically guided navigation policies and for measuring their performance across a range of social situations. More information can be seen at: https://anr-navware.github.io/navwareset/.

## Keywords

Social navigation, human-robot interaction, dataset, socially compliant navigation, non-compliant navigation

## Introduction

The rapid integration of autonomous robots into human-populated environments demands sophisticated capabilities for navigating in a socially compliant manner. In 2030, up to 20% of retail jobs (including malls services, restocking, last-mile delivery, guidance, etc.) could be carried out by robots navigating among humans (Manyika et al. 2017). Robots operating in these dynamic settings must not only avoid collisions but also adhere to social rules, such as respecting personal space, yielding appropriately, and ensuring smooth interactions. Social navigation has thus emerged as a critical research domain that requires robust datasets to train, evaluate, and benchmark navigation algorithms.

Existing datasets such as the Socially Compliant Navigation Dataset (SCAND) (Karnan et al. 2022) and THÖR (Rudenko et al. 2019) have laid essential groundwork by providing diverse perspectives on robot and human trajectories in both real-world and controlled environments. SCAND, for instance, captures robot navigation data in natural outdoor settings, while THÖR focuses on precise human motion trajectories in indoor spaces. However, these datasets often lack a systematic exploration of specific, well-defined social navigation scenarios and robot behaviors under controlled laboratory conditions.

To address these limitations, we introduce NavWareSet, a comprehensive dataset designed to capture both socially compliant and non-compliant navigation behaviors in controlled environments (Figure 1). NavWareSet includes seven distinct navigation scenarios, carefully selected to replicate challenges encountered in real-world human-robot interactions. By leveraging two types of robots –

<figure><img src="image_2.png" /><figcaption>**Figure 1.** The experimental design of the NavWareSet dataset. The teleoperator (green) and Ground-Truth Recording Station (orange) are on a raised platform. The robot (red) moves centrally, with participants (blue) spread around.</figcaption></figure>

**Corresponding author:**

Zhi Yan
Email: zhi.yan@ensta.fr

<footnote n="1">¹CIAD UMR7533, UTBM, 90010 Belfort, France</footnote>

<footnote n="2">²Graduate Program in Electrical Engineering - Univ. Federal de Minas Gerais - Av. Antônio Carlos 6627, 31270-901, Belo Horizonte, MG, Brazil</footnote>

<footnote n="3">³Université Marie et Louis Pasteur, UTBM, CNRS, ICB UMR 6303, 90010 Belfort, France</footnote>

<footnote n="4">⁴Faculty of Electrical Engineering, Czech Technical University in Prague, Czechia</footnote>

<footnote n="5">⁵Inria, France</footnote>

<footnote n="6">⁶U2IS, ENSTA, Institut Polytechnique de Paris, France</footnote>
<table><caption>**Table 1.** Comparison of Social Navigation Datasets</caption><thead><tr><th>Dataset</th><th>Env.</th><th>Robot</th><th>Dur.</th><th>#Traj.</th><th>On-Robot Sensors</th><th>Off-Robot Sensors</th><th>Behavior</th></tr></thead><tbody><tr><td>SCAND</td><td>In/Out</td><td>Spot, Jackal</td><td>8.7 h</td><td>138</td><td>LiDAR, RGB</td><td>None</td><td>Compliant</td></tr><tr><td>THÖR</td><td>In</td><td>Mock-up</td><td>1 h</td><td>600+</td><td>RGB-D, LiDAR</td><td>Mocap, Eye-tracker</td><td>Compliant</td></tr><tr><td>ETH/UCY</td><td>Out</td><td>None</td><td>N/A</td><td>~750</td><td>None</td><td>Static cameras</td><td>-</td></tr><tr><td>L-CAS</td><td>In/Out</td><td>Pioneer 3-AT</td><td>49 min</td><td>N/A</td><td>LiDAR</td><td>None</td><td>Non-Compliant</td></tr><tr><td>JRDB</td><td>In/Out</td><td>JackRabbot</td><td>64 min</td><td>2000+</td><td>LiDAR, RGB-D</td><td>None</td><td>Compliant</td></tr><tr><td>SDD</td><td>Out</td><td>None</td><td>1 h</td><td>20,000+</td><td>None</td><td>Drone RGB</td><td>-</td></tr><tr><td>NavWareSet</td><td>In</td><td>HSR, Jackal</td><td>3.2 h</td><td>1000+</td><td>LiDAR, RGB-D</td><td>LiDAR, RGB</td><td>Compliant &amp; Non-Compliant</td></tr></tbody></table>

Clearpath's Jackal and Toyota's Human Support Robot (HSR) (Yamamoto et al. 2019) – and varying their navigation strategies, NavWareSet provides a diverse and versatile dataset suitable for training and benchmarking social navigation algorithms.

Through its methodical experimental design, NavWareSet aims to fill critical gaps in current datasets, offering multi-modal data that includes lidar scans, camera feeds, and trajectory information. This dataset supports the development of socially compliant navigation policies and enables comparative studies on the impact of non-social navigation behaviors.

In summary, this paper presents the following key contributions:

* A multimodal dataset of over 3 hours of robot-human interactions across seven scenarios, recorded with two complementary robot platforms (Toyota HSR and Clearpath Jackal).

* Data captured in both socially compliant and non-compliant navigation modes, enabling direct behavioral comparisons.

* High-quality human trajectory annotations synchronized with robot sensor streams, supporting fine-grained analysis.

* A case study showing how NavWareSet can calibrate the Social Force Model (SFM), demonstrating its utility beyond data collection.

The remainder of this paper is organized as follows. The next section reviews related work on human motion and robot navigation datasets. The following section introduces the design of NavWareSet, describing the experimental setup, robot platforms, sensing modalities, and participant configurations. After that, we present the dataset's main results and statistics. We then illustrate a case study in which NavWareSet is applied to calibrate the Social Force Model, highlighting its utility for benchmarking interaction models. Finally, the paper concludes with a discussion of limitations and directions for future work.

## Related work

The development of social navigation capabilities for autonomous robots has been significantly aided by datasets capturing human motion and human-robot interactions. These datasets have been applied to trajectory prediction, human-aware motion planning, and social interaction

modeling tasks. This section provides an overview of key datasets and their characteristics.

## Datasets of Human Motion Trajectories

Early datasets focused on people motion in indoor spaces, e.g., the ATC dataset Brscic et al. (2013), tracks human motion across 900 m² shopping mall over the course of several weeks. Datasets like ETH, UCY, and Stanford Drone focus on outdoor environments, offering pedestrian trajectories useful for studying human motion prediction and interaction dynamics. The ETH (Pellegrini et al. 2009) and UCY (Lerner et al. 2007) datasets provide trajectory data captured in public spaces using static cameras, emphasizing dense pedestrian scenarios. The Stanford Drone dataset (Robicquet et al. 2016) extends this by incorporating overhead drone footage to capture large-scale pedestrian interactions. While these datasets are rich in human motion data and have been used for robot social navigation benchmarking in the past (Biswas et al. 2022), they lack robot-specific interactions, limiting their applicability to social navigation tasks.

## Datasets for Robot Navigation

Several datasets focus on robot navigation in human-populated environments. The L-CAS People Dataset (Yan et al. 2017) provides annotated 3D LiDAR scans collected by a mobile robot as it navigates public indoor spaces. It supports long-range human detection and tracking, focusing on perception and online learning. While valuable for studying multi-target tracking and motion dynamics, it does not explicitly target socially compliant behaviors, lacking annotations or scenarios specific to social navigation.

The JRDB dataset (Martin-Martin et al. 2021) captures robot-centric visual data, supporting tasks such as human detection and trajectory prediction, primarily in indoor environments. However, its total duration and the diversity of social interaction contexts are limited compared to SCAND. Just as L-CAS, JRDB's focus is robot perception of humans and not social robot navigation.

SCAND (Karnan et al. 2022), on the other hand, is a large-scale dataset focused on socially compliant robot navigation. It includes 8.7 hours of multimodal data collected from human teleoperation of robots in real-world indoor environments. While it excels at capturing natural social navigation behaviors, it lacks the systematic scenario control needed for rigorous benchmarking.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** The schema of the data collection environment. The circles represent the positions of the operator (green), recording station (orange), and example positions of the participants (blue) and robot (red). Fixed obstacles are shown in gray.</figcaption></figure>

THÖR (Rudenko et al. 2019) focuses on precise indoor motion capture data with high-frequency annotations, enabling detailed analysis of social interactions. However, its controlled setting restricts the diversity of trajectories and behaviors captured. In a later work (Schreiter et al. 2025), THÖR was extended with more data captured in a similar way but in larger spaces with longer trajectories and multi-person group interactions.

## NavWareSet Contributions

NavWareSet addresses critical gaps in existing navigation datasets by combining systematic experimental control with diverse navigation scenarios. It captures both socially compliant and non-compliant behaviors across seven well defined and distinct scenarios using two robot platforms (HSR and Jackal). By offering data on both social and non-social navigation under controlled and repeatable conditions, NavWareSet enables rigorous benchmarking of navigation algorithms. This complements real-world datasets such as SCAND, which focuses on naturalistic demonstrations, and THÖR, which emphasizes high-fidelity interaction data. Furthermore, NavWareSet's multi-modal data streams – including LiDAR, RGB-D, and odometry – support a range of research tasks, from trajectory forecasting to quantitative human-robot interaction analysis.

## Methodology

This section outlines the experimental design, scenario configurations, robot platforms, and data modalities used during the data collection process.

### Experimental Design

Data collection was conducted in a controlled indoor environment to ensure reproducibility and systematic exploration of social navigation challenges. The schema of the environment can be seen in Figure 2. The scenarios were inspired by field studies and established literature (Francis et al. 2025), designed to reflect realistic navigation tasks encountered in human-robot interactions. Each scenario was applied to test specific social navigation behaviors, ranging from avoidance maneuvers to collaborative tasks.

A total of 48 scenes were recorded, divided into seven scenarios:

* **Frontal Approach:** A robot and a human approach each other head-on in a passable space.

* **Pedestrian Obstruction:** A pedestrian blocks the robot's path, intentionally or unintentionally.

* **Blind Corner:** A robot and a human encounter each other at a blind corner where visibility is limited.

* **Following Human:** A human guides a robot to a target location, acting as a leader.

* **Perpendicular Traffic:** The robot must cross the path of a moving pedestrian whose direction is perpendicular to its own.

* **Circular Traffic:** Both the robot and the pedestrians start in a circular pattern around a central point. Then, they move in paths that intersect at that central point.

* **Object Handover:** The robot performs an object delivery, handing an item to a human or receiving one from them.

Most scenarios were recorded in both socially compliant and non-compliant conditions, enabling detailed behavioral comparisons. Socially compliant behavior followed predefined rules – such as ensuring a minimal robot-person distance and avoiding abrupt movements – while non-compliant behavior involved direct time-optimal waypoint-to-waypoint navigation without any regard for nearby humans. The participants did not know in advance if the robot will be driven in compliant or non-compliant manner.

### Robot Platforms

Data collection for NavWareSet was carried out using two complementary mobile robot platforms, each offering distinct sensing capabilities and locomotion characteristics. Both robots were integrated into the same experimental setup to provide varied viewpoints and navigation styles, as illustrated in Figure 3.

* **Toyota Human Support Robot (HSR):** A multifunctional mobile manipulator designed specifically for domestic services.

* **Clearpath Jackal:** A compact, four-wheeled unmanned ground vehicle (UGV) customized for human-aware navigation in large public spaces (Okunevich et al. 2025, 2024, 2023).

## Sensors and Data Modalities

Each robot platform used in NavWareSet was equipped with a suite of onboard sensors, and multiple data streams were recorded during each scenario to support rich multimodal analysis. Additionally, an external Ground-Truth Recording Station (GRS), as shown in Figure 4, was used to capture environmental data and facilitate the accurate annotation of human trajectories.

### Toyota HSR:

* RGB and depth images from a head-mounted RGB-D sensor,

* stereo camera images from left and right head-mounted sensors,

* planar laser scans from a base-mounted LiDAR,

* robot velocity commands (linear and angular),

* static and dynamic occupancy grid maps,
<figure><img src="image_4.png" /><figcaption>**Figure 3.** The left robot is the Clearpath Jackal, and the right is the Toyota Human Support Robot.</figcaption></figure>

* full transform tree (TF) data for frame coordination and localization.

**Clearpath Jackal:**

* RGB images from a front-facing onboard camera,

* 3D point clouds from a 360-degree Velodyne LiDAR,

* 2D pose estimates from localization algorithms,

* Robot velocity commands (linear and angular),

* Static and dynamic occupancy grid maps,

* Camera calibration parameters,

* Full transform tree data for frame coordination and localization.

**GRS:**

* 3D LiDAR sensor mounted on a tripod, providing static external observations of the scene,

* Overhead RGB video camera for visual context and annotation reference,

* Extrinsic calibration files aligning the GRS reference frame with each robot's sensor frames,

* Time-synchronized recording of all sensor streams for post-processing and annotation alignment.

GRS was positioned at an elevated location in the environment (see Figure 2), ensuring a clear and comprehensive field of view over the interaction area. Its data played a critical role in the manual annotation of pedestrian trajectories and enabled the creation of high-fidelity ground truth representations that complement the robots' onboard sensing. By combining robot-centric and external observations, NavWareSet ensures both robust internal perception streams and accurate external validation references.

**Participants and Group Configurations**

Seventeen adult volunteers participated in the data collection process. All participants were informed about the nature of the study and signed an informed consent form before the experiments. Since we did not need to collect human

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Ground-Truth Recording Station</figcaption></figure>

feedback, we did not need to obtain an ethics committee opinion according to the regulations of the laboratory where the data were collected. Data collection was conducted indoors under supervision, ensuring the comfort and safety of all individuals involved.

To promote diversity in the recorded interactions and ensure a wide range of human behaviors, participants were organized into two main groups, each comprising five individuals. These groups engaged in the full set of core navigation scenarios, including *Frontal Approach*, *Pedestrian Obstruction*, *Blind Corner*, *Following Human*, *Perpendicular Crossing*, and *Circular Crossing*. Each group interacted with both the Clearpath Jackal and the Toyota HSR robots, with each scene recorded twice: once in a socially compliant navigation mode and once in a non-compliant mode.

In addition to the two main groups, five distinct participant pairs were formed specifically for the *Object Handover* scenario. This scenario was performed exclusively with the Toyota HSR, due to its manipulator arm. Each pair executed a socially interactive task involving the delivery or reception of an object, simulating collaborative service situations. Similar to prior studies on spatial preferences in robot approach behavior during assistive tasks (Dautenhahn et al. 2006; Dai et al. 2022), this setup aimed to reflect realistic human-robot interactions in helping contexts. A detailed table showing all scenes and their relative participants, robot behavior, navigation scenario, etc., can be found on the project's website¹.

Throughout the data collection, robot trajectories were teleoperated to replicate both socially aware and unaware behaviors. The use of different group configurations and scene repetitions enabled the dataset to capture rich and varied instances of human-robot interaction, essential for the study and modeling of social navigation.

This study falls within the category of laboratory experiments using social navigation scenarios, as it
was conducted in a controlled indoor environment with predefined tasks. Participants were informed about the study procedures and consented but were not told the specific objectives, which helped reduce participant bias and preserved more natural reactions to the robots. A key strength of this design is the ability to generate a high density of interactions in a short time frame, ensuring systematic and repeatable comparisons between socially compliant and non-compliant navigation behaviors. However, the main limitation is the reduced ecological validity, since controlled settings cannot fully replicate the complexity and spontaneity of real-world, in-the-wild encounters, although ecological validity was increased by using previously proposed social navigation scenarios in literature, what is often called *construct validity* (Francis et al. 2025).

## Data Generation

After the completion of each recording session, the acquired information was organized and processed through a structured data pipeline designed to ensure adherence to the FAIR Guiding Principles for scientific data management and stewardship (Wilkinson et al. 2016). This pipeline involved the organization of raw data, its processing into structured representations, and the generation of final annotated products that compose the NavWareSet dataset.

Two ROS bag files were generated for each scene: one containing data from the robot's onboard sensors and another from GRS. These files include synchronized streams of LiDAR data, RGB-D imagery, odometry, and inertial measurements. All files followed a standardized naming convention that can be used to check for scenario type, robot used, social compliance condition, and participant group. This structure facilitates automated indexing, batch processing, and cross-referencing between data sources.

The processing stage involved both automated and manual steps. For the GRS, point clouds were generated from the external 3D LiDAR. Human trajectories were manually annotated frame by frame using the CVAT² annotation tool on the projected point clouds. These annotations were exported in the Supervisely format and later converted to CSV files for integration with downstream tools and simulations. All annotations were reviewed to ensure temporal consistency and spatial accuracy.

## Results

The NavWareSet dataset captures a rich variety of human-robot interactions across diverse social navigation scenarios. In addition to its comprehensive structure and modular file organization, the dataset yields high-quality data products that reflect the spatial and temporal dynamics of real-world navigation tasks. This section presents the main outcomes derived from the dataset, including quantitative information and visualizations that validate the fidelity of the recorded data.

In total, NavWareSet covers seven scenarios recorded in 48 scenes, encompassing two robot platforms (Toyota HSR and Clearpath Jackal) and two robot behaviors (compliant and non-compliant). The final dataset features:

* More than **192 minutes** of raw interaction recordings,

* Over **172 minutes** of manually annotated pedestrian trajectories,

* More than **1000** unique pedestrian trajectory segments,

* Over **600** robot movement tracks, including odometry and control commands,

* Multi-modal data streams: LiDAR, RGB-D, stereo vision, odometry, and occupancy maps,

* Dual-mode navigation behavior: socially compliant and non-compliant for each scenario.

Each finalized scene in the NavWareSet dataset comprises the following synchronized files and metadata:

* **Robot ROS bag**: raw onboard sensor recordings, including LiDAR scans, RGB video (Figure 7), RGB-D, odometry, and TF transforms.

* **Ground-Truth ROS bag**: external recordings from the GRS station, featuring 3D point clouds and RGB video (Figure 8).

* **Annotated 3D point clouds**: GRS captures with manual pedestrian annotations in Supervisely JSON format, aligned with robot pose data.

* **Pose CSV**: time-synced 2D trajectories of both the robot and pedestrians - timestamped positions (x, y) and robot orientation (w) - extracted from the combined robot and annotation data.

* **Occupancy map data**: static obstacle maps exported as 2D point sets or binary mask formats.

* **Calibration/extrinsic files**: transforms establishing spatial alignment between the GRS frame, robot sensors, and global coordinate system (i.e. the world frame).

This structured and reproducible approach ensures that NavWareSet can support a wide range of applications, including the training and evaluation of social navigation algorithms, pedestrian behavior modeling, and simulation-based benchmarking. Detailed information and explanations on the content and structure of each data product can be found on the project website³ (specifically in the *Data Overview* section).

Figure 5 illustrates typical examples of visualizations derived from the dataset. These include point cloud reconstructions, onboard robot camera views, annotated trajectories over time, and occupancy-grid maps. The high fidelity and alignment between modalities make the dataset suitable for both model-based simulation and data-driven learning approaches.

Figure 6 overlays pedestrian and robot trajectories for two scenario pairs under social and non-social behaviors. In the Frontal Approach pair, the social run keeps the robot near the corridor center with lateral adjustments to yield and maintain interpersonal distance, while the non-social run stays straighter and disregards human presence. In the Blind Corner pair, the social run decelerates earlier and follows a wider turning radius before entry, whereas the non-social run cuts the corner and induces abrupt pedestrian evasions. These contrasts expose how the behavior setting reshapes interaction geometry and provide ground truth for planner evaluation.

To support reproducibility and facilitate usage by the broader research community, all visualization tools and data
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Examples of visualizations extracted from NavWareSet. Top left: LIDAR point cloud of the environment; Top right: sensory data captured by the robot; Bottom left: point cloud synchronized with pedestrian positions and robot pose; Bottom right: trajectories extracted from the robot and participants over the environment map.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 6.** Trajectories from four representative scenes. Top: HSR, Group 2, Frontal Approach in social (Scene 27, right) and non-social (Scene 34, left) modes. Bottom: Jackal, Group 1, Blind Corner in social (Scene 17, right) and non-social (Scene 23, left) modes. Colored lines show pedestrian tracks. Black lines show robot passes. Gray dots indicate the static map.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 7. Video feed from the perspective of the robot</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 8. Video feed from the perspective of the GRS</figcaption></figure>

handling scripts are made available through the dataset's public repository⁴. These tools enable the recreation of the figures shown and allow researchers to seamlessly integrate the dataset into their own workflows. More information about the dataset can be accessed on its own website, and all data can be downloaded at the dat@UBFC portal⁵.

## Analysis

This section analyses how NavWareSet can be exploited to study robot social navigation and, in particular, to calibrate the Social Force Model (SFM) (Helbing and Molnár 1995). First, the importance of NavWareSet for robot social navigation is analyzed. Then, three dedicated subsections detail (i) the circular specification of the SFM adopted here, (ii) the objective function designed to tune its parameters and the numerical strategy employed, and (iii) the quantitative results of the optimisation, discussing the impact of using NavWareSet rather than synthetic or purely pedestrian-only data.

## Why NavWareSet Matters for Social Navigation

NavWareSet binds together multi-modal robot data (LiDAR, RGB-D, odometry) with externally-annotated human trajectories in seven repeatable scenarios. Because every scene is available in two flavours – **socially compliant** and **non-compliant** robot behaviour – researchers can:

1. **Benchmark planners**: re-run the recorded on-robot sensor streams through a new navigation stack and measure deviations from the compliant ground-truth.

2. **Learn interaction models**: fit human-aware costmaps or neural policies that map perception to socially acceptable control.

3. **Tune analytic models**: fine-tune closed-form crowd models such as the SFM under identical environmental conditions, enabling fair comparison against data-driven approaches.

The study below focuses on the third point: calibrating the SFM so that its simulated pedestrian responds to a moving robot exactly as the humans in NavWareSet did. Figure 5 already showed the rich set of aligned modalities that make such fitting possible.

## Circular Specification of the Social Force Model

The circular specification introduced by (Helbing and Johansson 2009) is adopted due to its widespread use in the robotics community and its ability to keep the number of free parameters tractable. For a pedestrian $\alpha$ and an entity $q \in \{\beta, i\}$ (another pedestrian or an obstacle/robot), the repulsive force is given by:

$$f_{\alpha q} = w(\phi_{\alpha q}) a \exp\left(-\frac{d_{\alpha q}}{b}\right) \hat{d}_{\alpha q}, \quad (1)$$

where $d_{\alpha q}$ is the Euclidean distance between pedestrian $\alpha$ and entity $q$, $\hat{d}_{\alpha q}$ is the unit vector pointing from $q$ to $\alpha$ (i.e., the direction of repulsion), $\phi_{\alpha q}$ is the angle between pedestrian $\alpha$'s walking direction and direction to the entity $q$, $a$ is the interaction strength (scaling factor for repulsive force), $b$ is the interaction range (decay factor of repulsion), and the $w(\phi_{\alpha q})$ is an angular modulation term defined as:

$$w(\phi_{\alpha q}) = \lambda + (1 - \lambda) \frac{1 + \cos \phi_{\alpha q}}{2}. \quad (2)$$

This weighting term captures the fact that pedestrians are more sensitive to interactions in their forward field of view than to those behind them. The parameter $\lambda \in [0, 1]$ controls the degree of anisotropy.

The total force acting on a pedestrian is composed of:

1. an attractive force $f_{\text{goal}}$ driving the pedestrian toward their goal,

2. the sum of repulsive forces from other pedestrians,

3. the sum of repulsive forces from obstacles or robots.

Formally, the net force is:

$$\mathbf{f}_{\text{total}} = \frac{1}{\tau_{\alpha}}(v^0 \mathbf{e}_{\alpha} - \mathbf{v}_{\alpha}) + \sum_{\beta \neq \alpha} \mathbf{f}_{\alpha \beta} + \sum_{i} \mathbf{f}_{\alpha i}, \quad (3)$$

where the first term represents the force driving the pedestrian towards his goal, with $\tau$ representing the relaxation time, i.e., how quickly a pedestrian adjusts its velocity, $\mathbf{e}_{\alpha}$ is the direction towards the goal and $v^0$ and $\mathbf{v}_{\alpha}$ are the desired and current walking speeds respectively. The terms $f_{\alpha q}$ of Eq. (3) are computed as in Eq. (1).

The pedestrian trajectories are then obtained by integrating $f_{\text{total}}$ over time using Newtonian mechanics, typically with a first-order update of velocity and position.
In the non-compliant version of the recordings, the influence of pedestrians on the robot's trajectory is disregarded. This simplifies mathematical modeling by treating robot behavior as independent of human actions.

## Parameter-Tuning Objective and Solver

The social model includes five tunable parameters:

$$ \Theta = \{\tau, v^0, a, b, \lambda\}. $$

Let $n \in \{1, \dots, N\}$ index the set of annotated tracks used for calibration. Each track provides time-stamped robot poses $\mathbf{r}^{(n)}(t)$ and pedestrian positions $\mathbf{p}_{\text{GT}}^{(n)}(t)$. For any candidate parameter vector $\Theta$ we forward-simulate the SFM with UAIbot (Brayan et al. 2025), yielding a predicted pedestrian trajectory $\mathbf{p}_{\text{sim}}^{(n)}(t|\Theta)$.

We minimize the cumulative Euclidean error over all tracks:

$$ J(\Theta) = \sum_{n=1}^{N} \sum_{t \in \mathcal{T}^{(n)}} \| \mathbf{p}_{\text{sim}}^{(n)}(t|\Theta) - \mathbf{p}_{\text{GT}}^{(n)}(t) \|_{2}^{2}. \quad (4) $$

$J(\Theta)$ is smooth but non-convex. We adopt the limited-memory BFGS with box constraints (L-BFGS-B) because (i) gradients are cheaply available by finite differences, thanks to fast UAIbot simulations, and (ii) the box bounds (cf. Table 2) enforce physically meaningful values based on previous literature.

<table><caption>**Table 2.** Lower and upper bounds used in the optimization of the SFM parameters.</caption><thead><tr><td>Parameter</td><td>Lower</td><td>Upper</td></tr></thead><tbody><tr><td>τ (relaxation time)</td><td>0.1</td><td>1.5</td></tr><tr><td>v<sup>0</sup> (desired speed)</td><td>0.5</td><td>2.5</td></tr><tr><td>a (repulsion amplitude)</td><td>0.5</td><td>8.0</td></tr><tr><td>b (repulsion decay)</td><td>0.5</td><td>8.0</td></tr><tr><td>λ (anisotropy)</td><td>0.0</td><td>1.0</td></tr></tbody></table>

## Optimised Parameters and Discussion

Applying the above procedure to fourteen *Frontal-Approach* tracks delivers the values seen on the first row of Table 3. The final cost $J^* = 346.3$ represents a mean position error of 0.24 m per time-step.

Compared with previous values from specialized literature (cf. Table 3) The larger $B = 5.17$ m confirms that humans begin to react to robots earlier in frontal approach scenarios, an effect also noted by (Farina et al. 2017). The shorter relaxation time $\tau$ captures their sharper deceleration once the robot gets truly close. Figure 9 qualitatively illustrates the match between a real track and its SFM reconstruction after optimisation.

These tuned parameters directly transfer to simulators – and ultimately motion planners – that rely on the SFM to predict human motion. Because they stem from NavWareSet's robot-human interactions, they are more appropriate for socially compliant navigation than parameters calibrated on pedestrian-only datasets. All the data and the code used on this section can be found at a dedicated repository⁶.

<table><caption>**Table 3.** Comparison between optimized SFM parameters (this work) and literature values.</caption><thead><tr><td>Source</td><td>a</td><td>b</td><td>λ</td><td>τ</td></tr></thead><tbody><tr><td>UAIbot (this work)</td><td>2.15</td><td>5.17</td><td>0.44</td><td>0.28</td></tr><tr><td>Ferrer and Sanfeliu (2013)</td><td>2.66</td><td>0.79</td><td>0.59</td><td>0.43</td></tr><tr><td>Agrawal et al. (2025)</td><td>7.93</td><td>0.99</td><td>0.40</td><td>0.60</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Figure 9.** Comparison between a trajectory obtained in NavWareSet (green) and one obtained with the SFM (blue).</figcaption></figure>

In summary, this analysis demonstrates that NavWareSet is not merely a collection of recordings but a practical instrument for quantitative research: it enables reproducible tuning of interaction models, bridges the gap between perception logs and navigation policies, and thus fosters more trustworthy social robots.

## Conclusion

This paper introduced NavWareSet, a novel dataset that addresses key limitations in existing resources for socially compliant robot navigation. By combining controlled, repeatable indoor scenarios with a diverse set of navigation behaviors – both socially compliant and non-compliant – NavWareSet offers a unique benchmark for the development, evaluation, and comparison of social navigation strategies.

The dataset includes multi-modal sensory data from two complementary robot platforms, high-quality annotations of human trajectories, and a rigorous experimental protocol involving a variety of human-robot interaction scenarios. With over 3 hours of recorded interactions and more than 1000 annotated pedestrian trajectory segments, NavWareSet enables fine-grained analysis of robot behavior in social contexts.

Beyond data collection, the paper demonstrated how NavWareSet can be leveraged to calibrate analytical interaction models, such as the Social Force Model (SFM), using real robot-human interactions.

Despite its strengths, NavWareSet also presents some limitations. The scenarios, while diverse and well defined, were recorded in a single indoor environment, which may
limit generalizability to other settings such as cluttered or
outdoor spaces. Additionally, while the dataset captures both
compliant and non-compliant robot behaviors, it does not
include autonomous navigation runs using planners with
learned policies, leaving a gap in evaluating real-time social
reasoning.

In future work, we plan to expand NavWareSet to include
dynamic, real-world deployments in unstructured public
spaces, as well as incorporate demonstrations from state-of-
the-art social navigation stacks. We also aim to extend the
SFM calibration framework into a modular benchmarking
toolkit, enabling the evaluation of multiple interaction
models under unified conditions.

NavWareSet is not only a repository of high-fidelity
navigation data but also a powerful tool for advancing
research in human-aware planning, trajectory prediction,
behavior learning, and simulation-based benchmarking. By
releasing the dataset and associated tools publicly, we aim to
foster reproducible, data-driven research and accelerate the
development of trustworthy and socially competent robotic
systems.

Notes

1. https://anr-navware.github.io/navwareset/

2. https://www.cvat.ai/

3. https://anr-navware.github.io/navwareset/

4. https://github.com/anr-navware/NavWareSet-Tutorials

5. https://search-data.ubfc.fr/FR-13002091000019-2025-05-22

6. https://github.com/anr-navware/NavWareSet-SFM

Acknowledgements

This project has received funding from the French National
Research Agency (ANR) under grant agreement No ANR-23-
CE10-0016. This study was financed in part by the Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES)
– Finance Code 001. We thank all those involved in the data
collection.

License

For the purpose of open access, a CC-BY public copyright
licence has been applied by the authors to the present
document and will be applied to all subsequent versions
up to the Author Accepted Manuscript arising from this
submission, in accordance with the grant's open access
conditions.

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