Datasetpublic

oscarqjh/VLN-CE-R2R_easi

VLN-CE R2R Dataset for EASI Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-to-Room (R2R) benchmark, repackaged for the 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… See the full description on the dataset page: https://huggingface.co/datasets/oscarqjh/VLN-CE-R2R_easi.

sourceHugging Facecc-by-nc-4.0updated 7mo agoView on Hugging Face

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Dataset Card

VLN-CE R2R Dataset for EASI

Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-to-Room (R2R) benchmark, repackaged for the 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, MOVEFORWARD (0.25m), TURNLEFT (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 (Krantz et al.)
—R2R Dataset (Anderson et al.)
—Matterport3D (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}
}