Update README.md

README.md

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 ---
 license: mit
+task_categories:
+  - robotics
+  - visual-question-answering
+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
+
+[![Paper](https://img.shields.io/badge/Paper-PDF-red?style=flat-square&logo=adobeacrobatreader&logoColor=white)](https://github.com/SemanticVLA/EmbodimentSemantic/blob/main/EmbodimentSemantic.pdf)
+[![Code](https://img.shields.io/badge/Code-GitHub-black?style=flat-square&logo=github)](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 |
+
 ---