| --- |
| license: mit |
| task_categories: |
| - robotics |
| - visual-question-answering |
| - image-text-to-text |
| language: |
| - en |
| tags: |
| - scene-graph |
| - spatial-reasoning |
| - robot-manipulation |
| - vla |
| - vlm |
| - libero |
| - lerobot |
| pretty_name: EmbodimentSemantic |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # EmbodimentSemantic |
|
|
| **A Spatial Scene-Graph Dataset and Benchmark for Vision-Language Models on Embodied Manipulation Trajectories** |
|
|
| Hassan Jaber¹ · Refinath S N² · Luca Cagliero¹ · Christopher E. Mower² · Haitham Bou-Ammar²³ |
|
|
| ¹Politecnico di Torino · ²Huawei Noah's Ark Lab · ³University College London |
|
|
| [](https://github.com/SemanticVLA/EmbodimentSemantic/blob/main/EmbodimentSemantic.pdf) |
| [](https://github.com/SemanticVLA/EmbodimentSemantic) |
|
|
| --- |
|
|
| ## Overview |
|
|
| EmbodimentSemantic is a benchmark dataset for evaluating whether vision-language models (VLMs) can recover exact spatial scene graphs from robot manipulation observations — and whether injecting those scene graphs into existing VLA policies improves downstream control. |
|
|
| The dataset has two components: |
|
|
| - **LIBERO Simulator Benchmark** — 500 demonstrations across 10 LIBERO-Spatial tasks (62,250 paired timesteps, 124,500 RGB frames). Ground-truth scene graphs are derived automatically from MuJoCo geometry, giving exact triplet-level supervision without manual annotation. |
| - **SO101 Real-Robot Dataset** — 257 teleoperated episodes across 5 tabletop bowl-placement tasks, collected with the low-cost SO101 arm. Includes external-camera, wrist-camera, and depth streams in LeRobot format. |
|
|
| --- |
|
|
| ## Files |
|
|
| | File | Size | Description | |
| |---|---|---| |
| | `libero_spatial_v5.zip` | 2.91 GB | LIBERO simulator benchmark: HDF5 demos with scene-graph annotations embedded under `obs/agentview_scene_graph` and `obs/robot0_eye_in_hand_scene_graph` | |
| | `SO1001_dataset.zip` | 6.13 GB | SO101 real-robot dataset in LeRobot format | |
|
|
| --- |
|
|
| ## Dataset Statistics |
|
|
| ### LIBERO Simulator Benchmark |
|
|
| | Attribute | Value | |
| |---|---| |
| | Tasks | 10 | |
| | Demonstrations | 500 (50 per task) | |
| | Recorded frames per camera | 62,250 | |
| | Total recorded frames (both cameras) | 124,500 | |
| | Frames per demo | 75–197 (mean 124.5) | |
| | Cameras | `agentview`, `eye_in_hand` | |
| | RGB resolution | 128 × 128 | |
| | Mean triplets / frame (agentview) | 42.0 | |
| | Mean triplets / frame (eye_in_hand) | 16.73 | |
|
|
| ### SO101 Real-Robot Dataset |
|
|
| | Attribute | Value | |
| |---|---| |
| | Tasks | 5 | |
| | Demonstrations | 257 (47–53 per task) | |
| | Total recorded frames (both cameras) | 240,598 | |
| | VLM eval frames (both cameras) | 8,252 | |
| | Cameras | `agent_view`, `wrist` | |
| | Frame rate | 30 FPS (1 frame/sec sampled) | |
| | Format | LeRobot | |
|
|
| --- |
|
|
| ## Spatial Ontology |
|
|
| **Objects (LIBERO):** `akita_black_bowl_1`, `akita_black_bowl_2`, `cookies_1`, `glazed_rim_porcelain_ramekin_1`, `plate_1`, `wooden_cabinet_1`, `flat_stove_1` |
|
|
| **Objects (SO101):** `black_bowl`, `red_drawer`, `black_stove`, `cookie`, `white_plate` |
|
|
| **Relations (both):** |
|
|
| | Relation | Description | |
| |---|---| |
| | `is_left_of` / `is_right_of` | Lateral world-frame ordering | |
| | `is_in_front_of` / `is_behind` | Depth world-frame ordering | |
| | `is_on_top_of` / `is_below_of` | Vertical support / stacking | |
| | `is_inside` / `contains` | Containment | |
|
|
| --- |
