Hugging Face's logo

Datasets:
standard-cognition
/
EvenFlow

Tasks:
Robotics
Other
Languages:
English
Size:
1K<n<10K
Tags:
navigation
human-robot-interaction
trajectory-data
multi-agent
benchmarking
License:
Dataset card Files
xet
 Community
Dataset Viewer
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
schema_version: string
scene_id: string
layout: struct<layout_id: string, path: string, coordinate_frame: string, notes: string>
  child 0, layout_id: string
  child 1, path: string
  child 2, coordinate_frame: string
  child 3, notes: string
window: struct<start: string, end: string, duration_s: double>
  child 0, start: string
  child 1, end: string
  child 2, duration_s: double
tracking: struct<tracking_id: string, format: string, path: string, timestamp_field: string, track_id_field: s (... 47 chars omitted)
  child 0, tracking_id: string
  child 1, format: string
  child 2, path: string
  child 3, timestamp_field: string
  child 4, track_id_field: string
  child 5, coordinate_frame: string
  child 6, notes: string
flow: struct<p_star: list<item: double>, u_hat: list<item: double>>
  child 0, p_star: list<item: double>
      child 0, item: double
  child 1, u_hat: list<item: double>
      child 0, item: double
metadata: struct<task_type: string, description: string, score: double, rerank_score: double, alignment_signed (... 332 chars omitted)
  child 0, task_type: string
  child 1, description: string
  child 2, score: double
  child 3, rerank_score: double
  child 4, alignment_signed: double
  child 5, crowd_coherence: double
  child 6, crowd_local_density: double
  child 7, difficulty_score_raw: double
  child 8, difficulty_score_norm: double
  child 9, difficulty_category: string
  child 10, difficulty_rank_within_category: int64
  child 11, heading_diff_deg: double
  
...
bject_duration_s: double
  child 14, crowd_n_other_tracks: int64
  child 15, crowd_n_samples: int64
provenance: struct<generator: struct<name: string, version: string>, generated_at: null, source: struct<chunk_fi (... 103 chars omitted)
  child 0, generator: struct<name: string, version: string>
      child 0, name: string
      child 1, version: string
  child 1, generated_at: null
  child 2, source: struct<chunk_file: string, person_track_id: string, window_start: string, window_end: string, candid (... 17 chars omitted)
      child 0, chunk_file: string
      child 1, person_track_id: string
      child 2, window_start: string
      child 3, window_end: string
      child 4, candidates_csv: string
obstacles: list<item: struct<id: string, polygon: list<item: list<item: double>>>>
  child 0, item: struct<id: string, polygon: list<item: list<item: double>>>
      child 0, id: string
      child 1, polygon: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
layout_id: string
exits: list<item: struct<id: string, polygon: list<item: list<item: double>>>>
  child 0, item: struct<id: string, polygon: list<item: list<item: double>>>
      child 0, id: string
      child 1, polygon: list<item: list<item: double>>
          child 0, item: list<item: double>
              child 0, item: double
boundary: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
version: string
units: string
to
{'layout_id': Value('string'), 'version': Value('string'), 'units': Value('string'), 'boundary': List(List(Value('float64'))), 'obstacles': List({'id': Value('string'), 'polygon': List(List(Value('float64')))}), 'exits': List({'id': Value('string'), 'polygon': List(List(Value('float64')))}), 'metadata': {'source_layout_id': Value('string'), 'source_version': Value('int64'), 'source_layout_api_version': Value('int64'), 'conversion_note': Value('string'), 'cleanup': {'merge_gap_m': Value('float64'), 'boundary_clearance_m': Value('float64'), 'min_area_m2': Value('float64'), 'regularization': Value('string'), 'inflation_limit': Value('float64'), 'notes': List(Value('string')), 'merge_report': List({'output_id': Value('string'), 'source_ids': List(Value('string')), 'regularization': Value('string'), 'area_before': Value('float64'), 'area_after': Value('float64'), 'inflation': Value('float64'), 'vertex_count_before': Value('int64'), 'vertex_count_after': Value('int64')})}, 'manual_family_collapse': {'default_mode': Value('string'), 'families': List({'ids': List(Value('string')), 'mode': Value('string')}), 'report': List({'output_id': Value('string'), 'source_ids': List(Value('string')), 'mode': Value('string'), 'input_area_sum': Value('float64'), 'output_area': Value('float64'), 'inflation': Value('float64')})}, 'manual_concave_replacements': {'obs_3': {'source_ids': List(Value('string')), 'mode': Value('string')}, 'obs_15': {'source_ids': List(Value('string')), 'mode': Value('string'), 'polygon': List(List(Value('float64')))}}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              schema_version: string
              scene_id: string
              layout: struct<layout_id: string, path: string, coordinate_frame: string, notes: string>
                child 0, layout_id: string
                child 1, path: string
                child 2, coordinate_frame: string
                child 3, notes: string
              window: struct<start: string, end: string, duration_s: double>
                child 0, start: string
                child 1, end: string
                child 2, duration_s: double
              tracking: struct<tracking_id: string, format: string, path: string, timestamp_field: string, track_id_field: s (... 47 chars omitted)
                child 0, tracking_id: string
                child 1, format: string
                child 2, path: string
                child 3, timestamp_field: string
                child 4, track_id_field: string
                child 5, coordinate_frame: string
                child 6, notes: string
              flow: struct<p_star: list<item: double>, u_hat: list<item: double>>
                child 0, p_star: list<item: double>
                    child 0, item: double
                child 1, u_hat: list<item: double>
                    child 0, item: double
              metadata: struct<task_type: string, description: string, score: double, rerank_score: double, alignment_signed (... 332 chars omitted)
                child 0, task_type: string
                child 1, description: string
                child 2, score: double
                child 3, rerank_score: double
                child 4, alignment_signed: double
                child 5, crowd_coherence: double
                child 6, crowd_local_density: double
                child 7, difficulty_score_raw: double
                child 8, difficulty_score_norm: double
                child 9, difficulty_category: string
                child 10, difficulty_rank_within_category: int64
                child 11, heading_diff_deg: double
                
              ...
              bject_duration_s: double
                child 14, crowd_n_other_tracks: int64
                child 15, crowd_n_samples: int64
              provenance: struct<generator: struct<name: string, version: string>, generated_at: null, source: struct<chunk_fi (... 103 chars omitted)
                child 0, generator: struct<name: string, version: string>
                    child 0, name: string
                    child 1, version: string
                child 1, generated_at: null
                child 2, source: struct<chunk_file: string, person_track_id: string, window_start: string, window_end: string, candid (... 17 chars omitted)
                    child 0, chunk_file: string
                    child 1, person_track_id: string
                    child 2, window_start: string
                    child 3, window_end: string
                    child 4, candidates_csv: string
              obstacles: list<item: struct<id: string, polygon: list<item: list<item: double>>>>
                child 0, item: struct<id: string, polygon: list<item: list<item: double>>>
                    child 0, id: string
                    child 1, polygon: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
              layout_id: string
              exits: list<item: struct<id: string, polygon: list<item: list<item: double>>>>
                child 0, item: struct<id: string, polygon: list<item: list<item: double>>>
                    child 0, id: string
                    child 1, polygon: list<item: list<item: double>>
                        child 0, item: list<item: double>
                            child 0, item: double
              boundary: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              version: string
              units: string
              to
              {'layout_id': Value('string'), 'version': Value('string'), 'units': Value('string'), 'boundary': List(List(Value('float64'))), 'obstacles': List({'id': Value('string'), 'polygon': List(List(Value('float64')))}), 'exits': List({'id': Value('string'), 'polygon': List(List(Value('float64')))}), 'metadata': {'source_layout_id': Value('string'), 'source_version': Value('int64'), 'source_layout_api_version': Value('int64'), 'conversion_note': Value('string'), 'cleanup': {'merge_gap_m': Value('float64'), 'boundary_clearance_m': Value('float64'), 'min_area_m2': Value('float64'), 'regularization': Value('string'), 'inflation_limit': Value('float64'), 'notes': List(Value('string')), 'merge_report': List({'output_id': Value('string'), 'source_ids': List(Value('string')), 'regularization': Value('string'), 'area_before': Value('float64'), 'area_after': Value('float64'), 'inflation': Value('float64'), 'vertex_count_before': Value('int64'), 'vertex_count_after': Value('int64')})}, 'manual_family_collapse': {'default_mode': Value('string'), 'families': List({'ids': List(Value('string')), 'mode': Value('string')}), 'report': List({'output_id': Value('string'), 'source_ids': List(Value('string')), 'mode': Value('string'), 'input_area_sum': Value('float64'), 'output_area': Value('float64'), 'inflation': Value('float64')})}, 'manual_concave_replacements': {'obs_3': {'source_ids': List(Value('string')), 'mode': Value('string')}, 'obs_15': {'source_ids': List(Value('string')), 'mode': Value('string'), 'polygon': List(List(Value('float64')))}}}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

EvenFlow Benchmark Dataset

EvenFlow is an evaluation suite for shared-space navigation, built from real-world human trajectory data.

Most benchmarks evaluate whether an agent can navigate around people.
EvenFlow evaluates whether an agent can navigate with them.

It converts real-world human trajectories into executable navigation tasks, enabling evaluation of coordination, timing, and interaction—not just collision avoidance.

Dataset Download. 

git clone https://huggingface.co/datasets/standard-cognition/EvenFlow

What This Dataset Provides

The dataset consists of:

  • Layouts: static environment geometry
  • Scenes: human trajectory data over time
  • Tasks: executable navigation problems derived from real behavior
  • Tracks: time-indexed human trajectories

These components are structured to support trajectory-level evaluation of navigation planners.

Dataset Structure 

benchmark/
  aligned_flow/
    tasks/
    scenes/
    layouts/
  cross_flow/
    tasks/
    scenes/
    layouts/
  interaction_constrained/
    tasks/
    scenes/
    layouts/

Each scenario family captures a different navigation regime:

  • Aligned Flow: motion aligned with surrounding traffic
  • Cross Flow: traversal across moving streams
  • Interaction-Constrained: navigation shaped by local human interactions

How to Use This Dataset

EvenFlow is designed for executable evaluation, not just analysis.

Typical workflow:

  1. Load a task (task.json)
  2. Resolve its scene and associated human trajectories
  3. Run a planner to generate a time-parameterized trajectory
  4. Evaluate the result using EvenFlow metrics

Code and evaluation tools:

👉 https://github.com/standard-ai/evenflow-benchmark

Responsible AI Considerations

Data Collection

Data was collected in real-world environments using overhead camera systems.
The dataset reflects naturally occurring human behavior in shared spaces.

Privacy

  • No raw video is released
  • No biometric identifiers are included
  • No personally identifiable information (PII) is present

All released data consists of anonymized trajectory representations.

Intended Use

This dataset is intended for research in:

  • robot navigation in human environments
  • multi-agent coordination
  • trajectory-based evaluation
  • human-aware motion planning

Limitations

  • Data is collected from a single physical environment (v1 release)
  • No demographic or identity-related attributes are included
  • Evaluation is performed offline (no closed-loop interaction)

We view this dataset as a foundation for broader multi-environment and interactive benchmarks.

License

This dataset is released under a custom research license.

  • Free for research and academic use
  • Commercial use requires a separate agreement

See the LICENSE file for full terms.
Downloads last month
28
Total file size:
129 MB