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README.md
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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 | `
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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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| 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 | `List[List[int]]` | 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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