--- license: mit pretty_name: BEHAVIOR-Spatial task_categories: - visual-question-answering - robotics tags: - spatial-reasoning - embodied-ai - behavior-1k - lerobot - arxiv:2610.34051 --- # BEHAVIOR-Spatial Benchmark Data for **BEHAVIOR-Spatial Benchmark: Evaluating Spatial Understanding for Robotics** ([arXiv:2610.34051](https://arxiv.org/abs/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 | |---|---|---| | **BEHAVIOR-Spatial-VQA** (Understand) | multiple-choice spatial questions | 3,256 questions: 1,572 general + 1,082 manipulation-task + 602 navigation-task | | **BEHAVIOR-Spatial-IR** (Ground) | point-based target grounding: predict the instructed target's image point | 1,240 queries over 14 manipulation tasks | | **BEHAVIOR-Spatial-Action** (Act) | sim evaluation instances and LeRobot training sets | 14 manipulation + 10 navigation tasks | Project website: https://arielchen07.github.io/behavior-spatial/ Code: https://github.com/arielchen07/BEHAVIOR-Spatial-benchmark ## Layout ``` vqa/ general/single_frame/ 1,078 questions (direction 600 / distance 339 / size 139), images/ general/multi_frame/rotation/ 239 questions (direction / distance / size) general/multi_frame/navigation/ 255 questions task/manipulation// 1,082 task-grounded questions over 14 manipulation tasks task/navigation// 602 task-grounded questions over 10 navigation tasks (+ context/ frames) ir/ gt/.json 1,240 grounding queries (14 tasks) frames// 720x720 head-camera frames masks// 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 (4,833 unique; 14 tasks + training-only place_saucer) 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 it. - **`meta/ir_centers.jsonl`** (manipulation) gives the per-frame target center in 720×720 head-image pixels (`null` = out of view; placement tasks use the destination). It is all you need to render the IR at any resolution. - **Navigation is head-camera only**, matching the paper's navigation policies. - **Manipulation training covers 15 tasks**: the 14 benchmark tasks plus `place_saucer` (97 episodes), which is used only for training, as in the paper. - **Repeated episodes.** The 5,532 manipulation episodes are 4,833 unique demonstrations plus 699 exact copies, kept so the set matches what the paper's policies trained on: variant balancing for `reach_food_item` (416) and `place_bowl_in_cabinet` (89), and upweighting of the approach-augmented `reach_paperback_book` episodes (194). `meta/duplicate_of.json` maps each copy to its original; drop its keys for unique demonstrations. `task_weights.json` adds per-task sampling weights on top (default preset `uniform_by_task`). 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/.json` records carry `instruction`, `gt_target`, `candidates`, `image_path` and per-candidate `bboxes_2d` (720×720 pixels), plus the camera and scene geometry the scorer uses. Placement tasks are scored on the destination; tolerant and strict accuracy are defined in the paper. 12 tasks also have a `mask_path`: a single-channel 720×720 PNG of instance labels (pixel value `i` = `mask_names[i-1]`, `0` = background; it looks black in an image viewer). A predicted point scores on the object whose mask it lands on. `reach_paperback_book` and `reach_hardback_from_bookcase` are scored on the boxes. ## 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). Each instance's `goal_conditions` sets how it is scored. The VQA prompt builder, the VQA / IR scorers and the simulator harness are in the [code release](https://github.com/arielchen07/BEHAVIOR-Spatial-benchmark). ## 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](https://behavior.stanford.edu/) (non-commercial academic research only). ## Citation ```bibtex @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} } ```