| --- |
| pretty_name: EvenFlow Benchmark |
| license: other |
|
|
| task_categories: |
| - robotics |
| - other |
|
|
| tags: |
| - navigation |
| - human-robot-interaction |
| - trajectory-data |
| - multi-agent |
| - benchmarking |
|
|
| annotations_creators: |
| - no-annotation |
|
|
| source_datasets: |
| - original |
|
|
| language: |
| - en |
|
|
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 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. |
|
|
