Datasetpublic

oscarqjh/VLN-CE-RxR_easi

VLN-CE RxR Dataset for EASI Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-across-Room (RxR) benchmark, repackaged for the EASI evaluation framework. Task An agent receives a natural language navigation instruction (in English, Hindi, or Telugu) 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 (30 deg), TURN_RIGHT (30 deg), LOOK_UP… See the full description on the dataset page: https://huggingface.co/datasets/oscarqjh/VLN-CE-RxR_easi.

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

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Dataset Card

VLN-CE RxR Dataset for EASI

Vision-and-Language Navigation in Continuous Environments (VLN-CE) Room-across-Room (RxR) benchmark, repackaged for the EASI evaluation framework.

Task

An agent receives a natural language navigation instruction (in English, Hindi, or Telugu) and must navigate through a Matterport3D indoor environment to reach a goal location. The agent uses discrete actions: STOP, MOVEFORWARD (0.25m), TURNLEFT (30 deg), TURNRIGHT (30 deg), LOOKUP (30 deg), LOOK_DOWN (30 deg).

Success is measured when the agent stops within 3.0m of the goal position.

Splits

Split	Episodes
`val_seen_en`	2255
`val_seen_hi`	2223
`val_seen_te`	2268
`val_unseen_en`	3669
`val_unseen_hi`	3669
`val_unseen_te`	3668
Total	17752

Note: Only guide-role episodes from val_seen and val_unseen splits are included. Train and testchallenge splits are excluded (train is not needed for evaluation; testchallenge requires server submission with no public ground truth).

Languages: English (en-IN + en-US merged as en), Hindi (hi), Telugu (te).

Data Fields

Each episode row contains:

Field	Type	Description
`id`	int	Sequential index within split-language file
`episode_id`	string	Original VLN-CE episode ID
`scene_id`	string	Matterport3D scene name
`instruction`	string	Natural language navigation instruction
`language`	string	Original language code (en-IN, en-US, hi-IN, te-IN)
`role`	string	Episode role (always "guide")
`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)	Dense ground truth waypoints
`gt_actions`	string (JSON)	Ground truth action sequence

Scene Data

mp3d_scenes.zip contains Matterport3D .glb and .navmesh files for the 87 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.

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.)
—RxR Dataset (Ku 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}
}
@inproceedings{ku2020rxr,
  title={Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding},
  author={Alexander Ku and Peter Anderson and Roma Patel and Eugene Ie and Jason Baldridge},
  booktitle={EMNLP},
  year={2020}
}