Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# HORA: Hand–Object to Robot Action Dataset
+
+## Dataset Summary
+
+**HORA (Hand–Object to Robot Action)** is a large-scale **multimodal** dataset that converts human **hand–object interaction (HOI)** demonstrations into **robot-usable supervision** for cross-embodiment learning. It combines HOI-style annotations (e.g., MANO hand parameters, object pose, contact) with embodied-robot learning signals (e.g., robot observations, end-effector trajectories) under a unified canonical action space. :contentReference[oaicite:5]{index=5} :contentReference[oaicite:6]{index=6}
+
+HORA is constructed from **three sources/subsets**:
+
+1. **HORA(Mocap)**: custom multi-view motion capture system with **tactile-sensor gloves** (includes tactile maps).
+2. **HORA(Recordings)**: custom RGB(D) HOI recording setup (no tactile).
+3. **HORA(Public Dataset)**: derived from multiple public HOI datasets and retargeted to robot embodiments (6/7-DoF arms). 
+
+Overall scale: **~150k trajectories** across all subsets. 
+
+---
+
+## Key Features
+
+- **Unified multimodal representation** across subsets, covering both HOI analysis and downstream robotic learning. 
+- **HOI modalities**: MANO hand parameters (pose/shape + global transform), object 6DoF pose, object assets, hand–object contact annotations. 
+- **Robot modalities**: wrist-view & third-person observations, and end-effector pose trajectories for robotic arms, all mapped to a canonical action space. 
+- **Tactile** (mocap subset): dense tactile map for both hand and object (plus object pose & assets). 
+
+---
+
+## Dataset Statistics
+
+| Subset | Tactile | #Trajectories | Notes |
+| --- | --- | --- | --- |
+| HORA(Mocap) | ✅ | 63,141 | 6-DoF object pose + assets + tactile map |
+| HORA(Recordings) | ❌ | 23,560 | 6-DoF object pose + assets |
+| HORA(Public Dataset) | ❌ | 66,924 | retargeted cross-embodiment robot modalities |
+| **Total** |  | **~150k** |  |
+
+## Supported Tasks and Use Cases
+
+HORA is suitable for:
+
+- **Imitation Learning (IL)** / **Visuomotor policy learning**
+- **Vision–Language–Action (VLA)** model training and evaluation
+- HOI-centric research: contact analysis, pose/trajectory learning, hand/object dynamics
+
+
+---
+
+## Data Format
+
+
+
+### Example Episode Structure
+
+Each episode/trajectory may include:
+
+**HOI fields**
+
+- `hand_mano`: MANO parameters (pose/shape, global rotation/translation)
+- `object_pose_6d`: 6DoF object pose sequence
+- `contact`: hand–object contact annotations
+- `object_asset`: mesh/texture id or path
+
+**Robot fields**
+
+- `obs_wrist_rgb` 
+- `obs_third_rgb` 
+- `ee_pose`: end-effector pose trajectory (SE(3))
+- `gripper`: gripper open/close command (optional)
+- `action_space`: canonical action space metadata
+
+**Tactile fields (mocap only)**
+
+- `tactile_hand`: dense tactile map (time × sensors/vertices)
+- `tactile_object`: dense tactile map
+
+
+---