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 (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 test_challenge splits are excluded (train is not needed for evaluation; test_challenge 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

@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}
}