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README.md
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<a href="https://leggedrobotics.github.io/navitrace_webpage/" target="_blank">
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🏠 Project
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<a href="#" target="_blank">
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📄 Paper
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<a href="#" target="_blank">
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💻 Code
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<a href="https://huggingface.co/spaces/leggedrobotics/navitrace_leaderboard" target="_blank">
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- Validation split (50%) for model fine-tuning.
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- Test split (50%) with hidden ground-truths for public leaderboard evaluation.
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- 🔎 **Annotation Quality:** All images and traces manually collected and labeled by human experts.
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- 🏅 **Evaluation Metric:** Semantic-aware Trace Score, combining Dynamic Time Warping distance,
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## Uses
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### Run Benchmark
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TODO
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### Model Training
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## Structure
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## Citation
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```
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TODO
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## Contact
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TODO
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<a href="https://leggedrobotics.github.io/navitrace_webpage/" target="_blank">
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🏠 Project
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</a>
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<a href="#TODO" target="_blank">
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📄 Paper
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</a>
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<a href="#TODO" target="_blank">
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💻 Code
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</a>
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<a href="https://huggingface.co/spaces/leggedrobotics/navitrace_leaderboard" target="_blank">
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- Validation split (50%) for model fine-tuning.
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- Test split (50%) with hidden ground-truths for public leaderboard evaluation.
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- 🔎 **Annotation Quality:** All images and traces manually collected and labeled by human experts.
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- 🏅 **Evaluation Metric:** Semantic-aware Trace Score, combining Dynamic Time Warping distance, goal endpoint error, and embodiment-conditioned semantic penalties.
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## Uses
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### Run Benchmark
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We provide a [notebook](#TODO) with example code on how to run this benchmark with an API model.
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You can use this as a template to adapt to your own model.
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Additionally, we host a public [leaderboard](https://huggingface.co/spaces/leggedrobotics/navitrace_leaderboard) where you can submit your model's results.
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### Model Training
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You can use the validation split to fine-tune models for this task.
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Load the dataset with `dataset = load_dataset("leggedrobotics/NaviTrace")` and use `dataset["validation"]` for training your model.
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See the next section for details on the dataset columns.
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## Structure
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| Column | Type | Description | Example |
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|-------------------|------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|
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| sample_id | `str` | Unique identifier of a scenario. | TODO |
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| task | `str` | Language instruction (English) solvable purely from the visual information, emphasizing cases where different embodiments behave differently, while still reflecting everyday scenarios. | |
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| embodiments | `List[str]` | All embodiments ("Human", "Legged Robot", "Wheeled Robot", "Bicycle") suitable for the task. | |
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| image | `PIL.Image` | First-person image of a real-world environment with blured faces and license plates. | |
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| segmentation_mask | `numpy.ndarray` | Semantic segmentation mask of the image generated with the [Mask2Former model](https://huggingface.co/facebook/mask2former-swin-large-mapillary-vistas-semantic). | |
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| ground_truth | `dict[str, `<br>`Optional[List[`<br>`List[List[float]]`<br>`]]]` | A dict mapping an embodiment name to a sequence of 2D points in image coordinates that describes a navigation path solution. One path per suitable embodiment, and multiple paths if equally valid alternatives exist (e.g., avoiding an obstacle from the left or right). If an embodiment is not suitable for the task, the value is `None`. | |
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| category | `List[str]` | List with one or more categories ("Semantic Terrain", "Geometric Terrain", "Stationary Obstacle", "Dynamic Obstacle", "Accessibility", "Visibility", "Social Norms") that describe the main challenges of the navigation task. | |
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| context | `str` | Short description of the scene as bullet points separated with ";", including the location, ongoing activities, and key elements needed to solve the task. | |
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| metadata | `dict[str, str]` | Additional information about the scenario:<br>- *"country":* The image's country of origin.<br>- *"city":* The image's city of origin or "GrandTour Dataset" if the image comes from the [Grand Tour dataset](https://grand-tour.leggedrobotics.com/).<br>- *"urban_rural":* "Urban", "Rural", or "Mixed" depending on the image's setting.<br>- *"natural_structured":* "Structured", "Natural", or "Mixed" depending on the image's environment.<br>- *"lighting_conditions":* "Night", "Daylight", "Indoor Lighting", or "Low Light" depending on the image's lighting.<br>- *"weather_conditions":* "Cloudy", "Clear", "Rainy", "Unknown", "Foggy", "Snowy", or "Windy" depending on the image's weather.<br>- *"task_type":* Distinguishes between instruction styles. Goal-Directed tasks ("Goal") specify the target explicitly (e.g., “Go straight to the painting.”), while Directional tasks ("Directions") emphasize the movement leading to it (e.g., “Move forward until you see the painting.”). Since this is ambiguous sometimes, there are also mixed tasks ("Mixed"). | |
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## Citation
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```
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TODO
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```
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