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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.
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