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
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 |