# 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'] ```