BEHAVIOR-Spatial Benchmark

Data for Behavior Spatial Benchmark: Evaluating Spatial Understanding for Robotics (arXiv:2610.34051). The benchmark measures whether spatial understanding in vision-language models (VLMs) carries over to vision-language-action (VLA) behavior. It has three components, all built in BEHAVIOR-1K / OmniGibson:

Component	What	Size
Understand: spatial VQA	multiple-choice spatial questions	3,256 questions: 1,572 general + 1,082 manipulation-task + 602 navigation-task
Ground: point-based target grounding (IR)	predict the instructed target's image point	1,240 queries over 14 manipulation tasks
Act: policy evaluation + training data	sim evaluation instances and LeRobot training sets	14 manipulation + 10 navigation tasks

Project website: https://arielchen07.github.io/behavior-spatial/

Layout

vqa/
  general/single_frame/            1,078 questions (spatial prefix / direction / object), images/
  general/multi_frame/rotation/      239 questions (direction / distance / size)
  general/multi_frame/navigation/    255 questions
  task/manipulation/<task>/        1,082 task-grounded questions over 14 manipulation tasks
  task/navigation/<task>/            602 task-grounded questions over 10 navigation tasks (+ context/ frames)
ir/
  gt/<task>.json                   1,240 grounding queries (14 tasks)
  frames/<task>/                   720x720 head-camera frames
  masks/<task>/                    instance-segmentation masks (12 tasks; see "IR ground truth")
action/
  eval/manipulation/held_out/      469 evaluation instances (BDDL + task-relevant-object JSON)
  eval/manipulation/prompt_probe/  700 instruction-change probes
  eval/navigation/held_out/        401 navigation instances
  train/manipulation/              LeRobot v2.1, 5,532 episodes
  train/navigation/                LeRobot v2.1, 3,912 episodes
  train/manipulation_subsets/      episode-index lists for the 30% / 50% data-scaling runs
  train/navigation_context/        context-tour frames per scene (21 / 6 / 13 / 16 / 21)
scripts/downscale_to_224.py        rebuild the 224x224 inputs the paper's policies trained on
MANIFEST.json                      per-component counts + sha256 of every JSON/JSONL file

Each VQA questions.json holds records with question_id, question, options, gt_answer, and image path(s) relative to that file's directory.

Training data (LeRobot v2.1)

action/train/{manipulation,navigation} load with stock LeRobot v2.1 (LeRobotDataset(repo_id, root=...)) and use the standard chunk-{episode_index // 1000:03d} layout.

State and action are 23-dim (base velocity 3, trunk 4, left arm 7, left gripper 1, right arm 7, right gripper 1) at 30 fps. Normalization statistics used in the paper are in norm_stats.json; per-task sampling weights in task_weights.json.

All video streams are at native resolution:

key	manipulation	navigation	resolution
head_image	✓	✓	720×720
left_wrist_image, right_wrist_image	✓		480×480
object_center_image (dot IR)	✓		720×720
object_center_first_image (frame-0 dot IR, repeated)	✓		720×720
attn_map_image (Gaussian attention-map IR)	✓		720×720

• Resolution used in the paper. The paper's policies were trained on a 224×224 downscale of these streams. scripts/downscale_to_224.py rebuilds that version. The RGB comes back to within ~39 dB PSNR of the trained-on frames (not byte-identical, since those were resized directly from the source recordings). The IR is re-rendered exactly with the 224 recipe: dot radius 4 px over a 1 px white outline, attention-map σ = 8 px.
• IR at 720 uses the same per-frame target centers with the geometry scaled by 720/224: dot radius 13 px over a 3 px outline, σ = 25.7 px.
• meta/ir_centers.jsonl (manipulation) gives, per episode and per frame, the target center in 720×720 head-image pixels (null = target outside the view). Placement tasks use the destination as the target. This file is all you need to render the IR at any resolution or in another style.
• Navigation is head-camera only, matching the paper's navigation policies.
• reach_paperback_book episodes include repeated copies of the same approach-augmented episode (upweighting), exactly as trained.

Sources: BEHAVIOR-1K 2025 and 2026 challenge demonstrations (behavior-1k/2025-challenge-demos, behavior-1k/2026-challenge-demos) plus simulator replays for reach_hardback_from_bookcase, part of reach_plate, and the approach-augmented reach_paperback_book episodes.

IR ground truth

ir/gt/<task>.json records carry instruction, gt_target, candidates, image_path and per-candidate bboxes_2d (720×720 pixels). gt_source says where the boxes come from: seg_mask (with mask_path, 12 tasks), projected_bbox (reach_paperback_book), or constructed_bbox (reach_hardback_from_bookcase). Placement tasks are scored on the destination; tolerant and strict accuracy are defined in the paper.

Each mask is a single-channel 720×720 PNG of instance labels: pixel value i is mask_names[i-1] of its record, 0 is background. The values are small, so the PNGs look black in an image viewer. A predicted point scores on the object whose mask it lands on; the boxes alone are not enough, because neighbouring objects' boxes overlap. The scorer (score_ir.py) is in the code release.

Evaluation instances

action/eval/** holds the BDDL task definitions and task-relevant-object (_tro.json) files that the simulator harness loads. The 2025-scene tasks run on OmniGibson 3.7.2; the four restaurant-diner navigation tasks need OmniGibson 3.9.2 / Isaac Sim 5.1 (BEHAVIOR-1K 2026 assets). The evaluation harness is released with the code.

License

This dataset is released under the MIT License, matching the BEHAVIOR-1K demonstrations it builds on. It contains rendered images and videos, annotations, and task definitions, but no 3D assets. Running the simulator evaluation requires the BEHAVIOR-1K scene and object assets, which are distributed separately under the BEHAVIOR Data Bundle End User License Agreement (non-commercial academic research only).

Citation

@article{chen2026behaviorspatial,
  title   = {Behavior Spatial Benchmark: Evaluating Spatial Understanding for Robotics},
  author  = {Chen, Sirui and Huang, Huang and Wang, Qineng and Huang, Wenlong and Li, Manling and Zhang, Ruohan and Fei-Fei, Li and Wu, Jiajun},
  journal = {arXiv preprint arXiv:2610.34051},
  year    = {2026}
}