Initial upload: 84 objects, 1861 successful grasps (R1+R2+R3+D1+D2+NEW)

.gitattributes

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README.md

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+# RobotPosterior Dataset
+
+Robotic grasp posterior data collected from Isaac Sim physical validation across multiple rounds.
+
+## Contents
+
+- **84 objects** (OakInk + DexYCB)
+- **1861 successful grasps** verified by physics simulation (Franka Panda in Isaac Sim)
+- Per-object HDF5 files with full grasp pose data
+
+## HDF5 Schema
+
+Each `{obj_id}_robot_gt.hdf5` contains:
+
+```
+/
+├── attrs:
+│   ├── obj_id          (str)
+│   ├── n_successful    (int)
+│   └── sources         (str)  e.g. "R1(×5) | R2(×13) | NEW(×20)"
+│
+└── successful_grasps/
+    ├── attrs: count
+    └── grasp_N/
+        ├── grasp_point         (3,)   float32  — contact midpoint, canonical mesh frame
+        ├── rotation            (3,3)  float32  — gripper rotation matrix
+        ├── approach_dir        (3,)   float32  — rotation[:, 2]
+        ├── finger_dir          (3,)   float32  — rotation[:, 0]
+        ├── [contact_points_local] (2,3) float32 — actual finger tip positions
+        └── attrs:
+            ├── gripper_width   (float)  meters
+            ├── score           (float)
+            └── approach_type   (str)
+```
+
+## Coordinate Frame
+
+- `grasp_point`, `rotation`: **canonical mesh local frame** (Z-up, object bottom at Z=0)
+- `contact_points_local`: world offset from `OBJECT_POSITION` in Isaac Sim
+
+## Data Sources
+
+| Label | Round | Server | Objects | Grasps |
+|-------|-------|--------|---------|--------|
+| R1 | Round 1 | Titan | 48 | ~231 |
+| R2 | Round 2 | Titan | 55 | ~592 |
+| R3 | Round 3 | Titan | 43 | ~181 |
+| D1 | DexYCB R1 | Titan | 7 | ~14 |
+| D2 | DexYCB R2 | Titan | 6 | ~41 |
+| NEW | Round 0+1 | RTX5090 | 70 | ~802 |
+
+## Candidate Generation
+
+Grasp candidates generated with **50% Human-Prior guided + 50% random** sampling (v5.1 Raycast scorer).  
+All entries physically verified in **Isaac Sim** with Franka Panda robot.
+
+## Usage
+
+```python
+import h5py, numpy as np
+
+with h5py.File('C28001_robot_gt.hdf5', 'r') as f:
+    print(f"n_successful: {f.attrs['n_successful']}")
+    for key in f['successful_grasps'].keys():
+        g = f['successful_grasps'][key]
+        grasp_point  = g['grasp_point'][:]    # (3,) meters, mesh local frame
+        approach_dir = g['approach_dir'][:]   # (3,) unit vector
+        width        = g.attrs['gripper_width']
+```