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Datasets:
Voxel51
/
navwareset

Tasks:
Other
Languages:
English
Size:
n<1K
Tags:
fiftyone
human-robot-interaction
lidar
mcap
multimodal
navigation
Libraries:
FiftyOne
License:
Dataset card Files
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Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 394 chars omitted)
  child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 382 chars omitted)
      child 0, _id: struct<$oid: string>
          child 0, $oid: string
      child 1, filepath: string
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      child 3, _media_type: string
      child 4, _rand: double
      child 5, scene_id: string
      child 6, scenario: string
      child 7, robot_platform: string
      child 8, behavior: string
      child 9, participant_group: string
      child 10, has_pedestrian_annotations: bool
      child 11, duration_s: double
      child 12, message_count: int64
      child 13, channel_count: int64
      child 14, topics: list<item: string>
          child 0, item: string
      child 15, schemas: list<item: string>
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      child 16, _dataset_id: struct<$oid: string>
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          child 0, $date: string
      child 18, last_modified_at: struct<$date: string>
          child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_targets: null
skeletons: null
camera_intrinsics: null
static_transforms: null
annotation_runs: null
brain_methods: null
evaluations: null
runs: null
la
...
pe: string
      child 3, subfield: string
      child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
          child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
              child 0, name: string
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          child 0, $date: string
workspaces: list<item: null>
  child 0, item: null
default_classes: list<item: null>
  child 0, item: null
sample_collection_name: string
last_loaded_at: struct<$date: string>
  child 0, $date: string
created_at: struct<$date: string>
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saved_views: list<item: null>
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active_label_schemas: list<item: null>
  child 0, item: null
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tags: list<item: null>
  child 0, item: null
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type:  (... 394 chars omitted)
                child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 382 chars omitted)
                    child 0, _id: struct<$oid: string>
                        child 0, $oid: string
                    child 1, filepath: string
                    child 2, tags: list<item: null>
                        child 0, item: null
                    child 3, _media_type: string
                    child 4, _rand: double
                    child 5, scene_id: string
                    child 6, scenario: string
                    child 7, robot_platform: string
                    child 8, behavior: string
                    child 9, participant_group: string
                    child 10, has_pedestrian_annotations: bool
                    child 11, duration_s: double
                    child 12, message_count: int64
                    child 13, channel_count: int64
                    child 14, topics: list<item: string>
                        child 0, item: string
                    child 15, schemas: list<item: string>
                        child 0, item: string
                    child 16, _dataset_id: struct<$oid: string>
                        child 0, $oid: string
                    child 17, created_at: struct<$date: string>
                        child 0, $date: string
                    child 18, last_modified_at: struct<$date: string>
                        child 0, $date: string
              group_media_types: null
              info: null
              app_config: null
              classes: null
              mask_targets: null
              default_mask_targets: null
              skeletons: null
              camera_intrinsics: null
              static_transforms: null
              annotation_runs: null
              brain_methods: null
              evaluations: null
              runs: null
              la
              ...
              pe: string
                    child 3, subfield: string
                    child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
                        child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
                            child 0, name: string
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                                child 0, item: null
                            child 5, db_field: string
                            child 6, description: null
                            child 7, info: null
                            child 8, read_only: bool
                            child 9, created_at: struct<$date: string>
                                child 0, $date: string
                    child 5, db_field: string
                    child 6, description: null
                    child 7, info: null
                    child 8, read_only: bool
                    child 9, created_at: struct<$date: string>
                        child 0, $date: string
              workspaces: list<item: null>
                child 0, item: null
              default_classes: list<item: null>
                child 0, item: null
              sample_collection_name: string
              last_loaded_at: struct<$date: string>
                child 0, $date: string
              created_at: struct<$date: string>
                child 0, $date: string
              saved_views: list<item: null>
                child 0, item: null
              slug: string
              active_label_schemas: list<item: null>
                child 0, item: null
              name: string
              version: string
              tags: list<item: null>
                child 0, item: null
              to
              {'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
              because column names don't match

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Dataset Card for NavWareSet

image/png

This is a FiftyOne dataset with 7 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage 

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/navwareset")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

NavWareSet is a multi-modal dataset of socially-compliant and non-compliant robot navigation around human pedestrians, collected in a controlled indoor environment (3.9 m × 10.7 m). The source dataset covers 48 scenes across seven canonical social-navigation scenarios (Frontal Approach, Pedestrian Obstruction, Blind Corner, Following Human, Perpendicular Traffic, Circular Crossing, Object Handover), each recorded with two robot platforms (Toyota Human Support Robot and Clearpath Jackal) and, for six of the seven scenarios, in both a socially-compliant and a non-compliant navigation mode under matched initial conditions. Every scene is captured simultaneously by the robot's own onboard sensors and by a stationary external Ground-truth Recording Station (GRS: a RoboSense RS-LiDAR-16 mounted above an Intel RealSense camera), which also provides the point cloud on which human trajectories were manually annotated.

This FiftyOne parse currently ships 7 of the source dataset's 48 scenes, Jackal only (Toyota HSR carries a head RGB-D sensor plus 2 stereo cameras and is not yet imported; the merge pipeline already supports it, see pipeline/merge_bags.py). Each sample is one scene, stored as a single multi-topic .mcap file combining the robot's onboard sensors, the GRS's sensors, and the manually-annotated pedestrian trajectories into one synchronized timeline, viewable in FiftyOne's multimodal 3D/Image/Plot tiles.

  • Curated by: Johnata Brayan, Sihao Deng, Armando Alves Neto, Iaroslav Okunevich, Tomas Krajnik, Francois Bremond, Zhi Yan (CIAD UMR7533/UTBM; Universidade Federal de Minas Gerais; Université Marie et Louis Pasteur/UTBM/CNRS ICB UMR 6303; Czech Technical University in Prague; Inria; ENSTA/Institut Polytechnique de Paris)
  • Funded by: French National Research Agency (ANR), grant ANR-23-CE10-0016; Toyota Partner Robot joint research project; Roboprox (Czechia), CZ.02.01.01/00/22_008/0004590; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brasil), Finance Code 001
  • Shared by: harpreetsahota (this FiftyOne parse); original dataset by Brayan et al.
  • Language(s): en
  • License: CC BY 4.0

Dataset Sources

Uses

Direct Use

Training and benchmarking social navigation algorithms; direct behavioral comparison between socially-compliant and non-compliant robot motion under matched initial conditions; pedestrian-response modeling and trajectory prediction conditioned on robot behavior; calibrating analytic human-motion/interaction models such as the Social Force Model against real robot-human recordings (the source paper demonstrates this with UAIbot); use as counterexamples (non-compliant trajectories) alongside compliant examples in imitation/avoidance-learning setups.

Out-of-Scope Use

The source data was collected in a single controlled indoor lab environment and does not generalize to outdoor, cluttered, or multi-room settings. It contains teleoperated trajectories only — no autonomous navigation runs from learned policies are included, so it is not suited to evaluating real-time onboard planners end-to-end. This FiftyOne parse specifically ships Jackal scenes only; it should not be used to draw conclusions about the Toyota HSR platform's behavior until HSR scenes are imported.

Dataset Structure

Media type: multimodal — one FiftyOne sample per scene, each backed by one .mcap file (Foxglove/ROS 2 MCAP, ros2msg/cdr message encoding for raw sensor topics, Foxglove protobuf schemas for authored annotation channels). 7 samples currently, spanning 2 of the source dataset's 7 participant groupings (Group 1, Group 2, and Pair 1–5 for Object Handover): Group 1 (scenes 15–18, 25) and Group 2 (scenes 46–47), and both behavior values.

Per-sample fields:

Field FiftyOne type Description
filepath StringField Path to the scene's .mcap file
scene_id StringField Source scene number (verbatim from source, e.g. "15")
scenario StringField One of the 7 canonical scenarios (verbatim from the project site's scene table — not shipped machine-readable in the source data)
robot_platform StringField "Jackal" for every sample currently shipped
behavior StringField "social" (compliant) or "non-social" (non-compliant)
participant_group StringField "Group 1" or "Group 2" — which 5 volunteers appear in the scene
has_pedestrian_annotations BooleanField True for all 7 (would be False only for the 5 Object Handover scenes, not yet imported)
duration_s FloatField Episode duration in seconds (~250s / ~4 min per scene)
message_count IntField Total MCAP message count (~78k per scene)
channel_count IntField Total MCAP channel/topic count (18 per scene)
topics ListField(StringField) Every topic name present in the scene's .mcap
schemas ListField(StringField) Every message schema name present in the scene's .mcap

Inside each .mcap, the 18 channels break down as:

Topic Schema Content
/grs/rslidar_points sensor_msgs/msg/PointCloud2 GRS RoboSense RS-LiDAR-16 point cloud (stationary), ~10 Hz
/grs/camera/color/image_raw sensor_msgs/msg/CompressedImage (JPEG) GRS overhead RGB video
/grs/camera/color/camera_info sensor_msgs/msg/CameraInfo GRS camera intrinsics
/grs/tf_static tf2_msgs/msg/TFMessage Authored GRS extrinsics chain (grs_map→grs_rslidar→grs_camera_color→optical frame)
/robot/rslidar_points sensor_msgs/msg/PointCloud2 Jackal onboard lidar, ~20 Hz
/robot/camera/color/image_raw sensor_msgs/msg/CompressedImage (JPEG) Jackal onboard front camera
/robot/camera/color/camera_info sensor_msgs/msg/CameraInfo Jackal camera intrinsics
/robot/tf tf2_msgs/msg/TFMessage Jackal's real recorded transform tree (odom↔base_link, wheel joints, camera/lidar mounts)
/robot/tf_static tf2_msgs/msg/TFMessage Authored Jackal camera optical-frame convention
/robot/amcl_pose geometry_msgs/msg/PoseWithCovarianceStamped Jackal's 2D AMCL localization estimate
/robot/cmd_vel geometry_msgs/msg/Twist Jackal velocity commands
/robot/map nav_msgs/msg/OccupancyGrid Jackal's onboard occupancy grid
/connector/tf_static tf2_msgs/msg/TFMessage Per-scene calibration linking the robot's localization frame to the GRS frame
/annotations/pedestrians foxglove.SceneUpdate Per-frame pedestrian 3D cuboids, persistent per-person track ID
/annotations/robot_pose foxglove.SceneUpdate Robot 2D pose marker (arrow primitive), GRS-frame-native
/annotations/trajectories foxglove.SceneUpdate Static, full-episode robot + participant trajectory polylines
/annotations/occupancy foxglove.PointCloud Static floor-plan point set
/telemetry/positions JSON Per-frame flat robot_x/robot_y/robot_yaw_rad plus up to 5 participants' x/y

Label types and why:

  • Pedestrian 3D cuboids map to foxglove.SceneUpdate entities rather than a per-sample fo.Detections field, because they are a time-synced channel meant to play back alongside the lidar/camera channels in FiftyOne's multimodal 3D tile. SceneUpdate entities carry a persistent entity ID across frames, which is exactly what the source annotation format's per-person UUID (objects[].key in the Supervisely export) already provides — no separate tracking field needed.
  • The robot's pose is logged as an arrow primitive inside a SceneUpdate channel (/annotations/robot_pose) rather than a keypoint field, mirroring the official devkit's own visualization convention.
  • Trajectories are logged once per episode as static polyline entities (matching the source paper's Figure 4, "trajectories extracted from the robot and participants over the environment map") rather than growing per-frame, since a SceneUpdate entity persists across playback until replaced.
  • The static floor plan is a raw point set (foxglove.PointCloud), not a fo.Segmentation mask, since that is the format the source occupancy-map export ships in.

No dataset-level info dict is populated by the ingest pipeline. Sensor extrinsics (GRS mount height/orientation, Jackal's camera optical-frame convention, the per-scene GRS↔robot calibration) are instead authored as static TF channels inside each episode's own .mcap (/grs/tf_static, /robot/tf_static, /connector/tf_static) rather than as dataset-level metadata, so they travel with each sample individually.

Parsing decisions of note (see pipeline/merge_bags.py's module docstring for the full, per-decision verification):

  • The robot bag reuses the GRS's own topic names and frame_id strings for its own sensors — every topic is prefixed /grs or /robot, and the two colliding frame_ids are renamed rather than left to collide.
  • Neither raw bag records the GRS's or the robot camera's static extrinsics; these are authored from real numbers (the devkit's own launch file, and the universal ROS optical-frame axis convention), not fabricated.
  • The GRS's map→rslidar pitch is authored as flat (0°), not the 15° downward tilt the source paper/devkit launch file specify — verified empirically against the raw point cloud, which is already gravity-level in its own frame; applying the stated 15° would slope a real flat floor by ~2 m across the room.
  • rgb8/bgr8 camera images are transcoded to JPEG-backed CompressedImage (quality 90); depth and other non-8-bit-3-channel encodings are left raw.
  • PointCloud2 messages are repacked to drop undeclared per-point padding present in the raw sensor driver output (verified bit-identical x/y/z/ intensity values through a full serialize/deserialize roundtrip before applying).
  • The robot's own recorded /tf carries real but non-physical roll/pitch/z noise on the odom→base_link and map→odom transforms (verified against the same bag's /robot/amcl_pose, which is exactly roll=pitch=z=0.0 on every message) — these two transforms are flattened to yaw-only rotation with z=0 on ingest; x/y translation and yaw are left untouched as real, meaningful odometry.

Dataset Creation

Curation Rationale

The source paper's stated goal is to fill a gap left by existing social navigation datasets (e.g. SCAND, THÖR), which lack a systematic exploration of well-defined social navigation scenarios and robot behaviors under controlled conditions. NavWareSet is designed around seven canonical, literature-grounded interaction scenarios, each recorded with matched socially-compliant and non-compliant robot behavior under comparable initial conditions, so that differences in robot–human interaction can be directly attributed to the presence or absence of social awareness in the robot's motion, rather than confounded by environment or task differences.

Source Data

Data Collection and Processing

For each scene, two ROS bag files were recorded simultaneously: one from the robot's onboard sensors (lidar, RGB-D/stereo or single color camera, odometry, velocity commands, TF), and one from the external GRS station (3D lidar point clouds and overhead RGB video). Human trajectories were manually annotated frame-by-frame on the GRS's point clouds using the CVAT annotation tool, exported in Supervisely JSON format, and later converted to CSV for downstream tooling. Static occupancy maps were exported as 2D point sets. All files follow a standardized per-scene naming convention encoding scenario, robot, social-compliance condition, and participant group.

This FiftyOne parse re-processes the two raw ROS bags per scene into a single synchronized .mcap and layers the annotation products on top — see "Parsing decisions of note" above for the specific fixes this required.

Who are the source data producers?

Two mobile robot platforms — Toyota Human Support Robot (HSR) and Clearpath Jackal — each teleoperated to replicate both socially-aware and unaware navigation behavior. Seventeen adult volunteer participants, organized into two main groups of five plus five additional pairs for the Object Handover scenario, acted as pedestrians/interaction partners.

Annotations

Annotation process

Human (pedestrian) 3D positions were manually annotated frame-by-frame on the GRS's point clouds using the CVAT annotation tool, exported in Supervisely format (single Person class, 3D cuboid geometry, one persistent object key per tracked individual). This FiftyOne parse carries those cuboids through as a foxglove.SceneUpdate channel keyed by that same persistent per-person ID (see "Label types and why" above).

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

All 17 volunteer participants were informed about the nature of the study and signed an informed consent form before the experiments; no subjective personal feedback (e.g. discomfort/frustration questionnaires) was collected. The dataset does contain personally identifiable information in the form of RGB video of participants (both the GRS's overhead camera and the robot's onboard camera) and their 3D trajectories/positions throughout each scene.

Citation

BibTeX:

@article{brayan2026navwareset,
  title={NavWareSet: A Dataset of Socially Compliant and Non-Compliant Robot Navigation},
  author={Brayan, Johnata and Deng, Sihao and Alves Neto, Armando and Okunevich, Iaroslav and Krajnik, Tomas and Bremond, Francois and Yan, Zhi},
  journal={The International Journal of Robotics Research},
  year={2026},
  doi={10.1177/02783649261447305},
  note={HAL Id: hal-05231729v2, https://hal.science/hal-05231729v2}
}

APA:

Brayan, J., Deng, S., Alves Neto, A., Okunevich, I., Krajnik, T., Bremond, F., & Yan, Z. (2026). NavWareSet: A dataset of socially compliant and non-compliant robot navigation. The International Journal of Robotics Research. https://doi.org/10.1177/02783649261447305

More Information

This FiftyOne parse currently ships 7 of the source dataset's 48 scenes, Jackal platform only (scene 01, an HSR scene, was downloaded only to validate the merge pipeline and was never authored into this dataset). 14 more Jackal scenes exist in the source data and are not yet imported: 19, 20, 21, 22, 23, 26 (Group 1) and 41, 42, 43, 44, 45, 49, 51, 52 (Group 2). Toyota HSR scenes and the 5 Object Handover scenes (no pedestrian annotations) are also not yet imported. The merge/authoring pipeline supports all of these without code changes — see pipeline/scene_metadata.py for the full scene → scenario/robot/behavior/group table and AGENT_BOARDING.md for the import pipeline's decision log.

Official devkit and tutorials: https://github.com/anr-navware/NavWareSet-Tutorials

For the original dataset: Zhi Yan (zhi.yan@ensta.fr)

Dataset Card Authors

harpreetsahota (this FiftyOne parse)

Dataset Card Contact

For the original dataset: Zhi Yan (zhi.yan@ensta.fr)
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