Add VLN-CE R2R dataset (6025 episodes, 3 splits)

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+  - robotics
+tags:
+  - embodied-ai
+  - vision-language-navigation
+  - habitat-sim
+  - matterport3d
+  - vln-ce
+pretty_name: "VLN-CE R2R (EASI)"
+size_categories:
+  - 1K<n<10K
+---
+
+# VLN-CE R2R Dataset for EASI
+
+Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-to-Room
+(R2R) benchmark, repackaged for the [EASI](https://github.com/your-org/EASI)
+evaluation framework.
+
+## Task
+
+An agent receives a natural language navigation instruction and must navigate
+through a Matterport3D indoor environment to reach a goal location. The agent
+uses discrete actions: STOP, MOVE_FORWARD (0.25m), TURN_LEFT (15 deg),
+TURN_RIGHT (15 deg).
+
+Success is measured when the agent stops within 3.0m of the goal position.
+
+## Splits
+
+| Split | Episodes |
+|-------|----------|
+| `val_seen` | 778 |
+| `val_unseen` | 1839 |
+| `test` | 3408 |
+| **Total** | **6025** |
+
+**Note:** The `test` split does not include ground truth trajectories.
+SR and SPL can be computed locally, but NDTW/SDTW require ground truth
+and are only available for `val_seen` and `val_unseen`.
+
+## Data Fields
+
+Each episode row contains:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | int | Sequential index within split |
+| `episode_id` | string | Original VLN-CE episode ID |
+| `scene_id` | string | Matterport3D scene name |
+| `instruction` | string | Natural language navigation instruction |
+| `start_position` | list[float] | Agent start [x, y, z] in meters |
+| `start_rotation` | list[float] | Agent start quaternion [x, y, z, w] |
+| `goal_position` | list[float] | Goal [x, y, z] in meters |
+| `goal_radius` | float | Success radius (3.0m) |
+| `reference_path` | string (JSON) | Waypoint path from start to goal |
+| `geodesic_distance` | float | Shortest path distance in meters |
+| `gt_locations` | string (JSON) or null | Dense ground truth waypoints (null for test) |
+| `gt_actions` | string (JSON) or null | Ground truth action sequence (null for test) |
+
+## Scene Data
+
+`mp3d_scenes.zip` contains Matterport3D `.glb` and `.navmesh` files for the
+85 scenes used across all evaluation splits. After extraction:
+
+```
+mp3d/
+  {scene_id}/
+    {scene_id}.glb        # 3D mesh for Habitat-Sim rendering
+    {scene_id}.navmesh    # Navigation mesh for pathfinding
+```
+
+Only `.glb` (rendering) and `.navmesh` (pathfinding) are included.
+Semantic files (`.house`, `_semantic.ply`) are not needed for VLN-CE R2R
+since goals are specified as coordinates, not semantic object targets.
+
+## Metrics
+
+| Metric | Description |
+|--------|-------------|
+| SR | Success Rate (agent within 3.0m of goal) |
+| SPL | Success weighted by Path Length |
+| NDTW | Normalized Dynamic Time Warping (path shape similarity) |
+| SDTW | Success-weighted NDTW |
+| Oracle SR | Best achievable success along trajectory |
+| NE | Navigation Error (distance to goal at episode end) |
+| Path Length | Total distance traveled |
+
+## Original Sources
+
+- [VLN-CE](https://github.com/jacobkrantz/VLN-CE) (Krantz et al.)
+- [R2R Dataset](https://bringmeaspoon.org/) (Anderson et al.)
+- [Matterport3D](https://niessner.github.io/Matterport/) (Chang et al.)
+
+## Citation
+
+```bibtex
+@inproceedings{krantz2020vlnce,
+  title={Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments},
+  author={Jacob Krantz and Erik Wijmans and Arjun Majumdar and Dhruv Batra and Stefan Lee},
+  booktitle={ECCV},
+  year={2020}
+}
+```

mp3d_scenes.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa93a1145b95b8d96fa4a88cafb202dc309916d35450744919ad05d95c7dbd31
+size 6055603582