---
license: cc-by-4.0
---

# RealArt-6

This is the official dataset collected for [RPMArt](https://r-pmart.github.io/) to test the sim-to-real transfer. It contains 6 articulated object instances, each captured from 20 camera views under 5 states in scenarios with and without background, as well as presence or absence of distractors.

## Dataset Structure
```
without_table
├── microwave
│   ├── 0_without_chaos
│   │   ├── xyzrgb_00.npz         # microwave point cloud from 00 camera view under 0 state in scenario without background and without distractors
│   │   ├── xyzrgb_01.npz         # microwave point cloud from 01 camera view under 0 state in scenario without background and without distractors
│   │   └── ...
│   ├── 1_without_chaos
│   ├── ...
│   ├── 0_with_chaos
│   │   ├── xyzrgb_00.npz         # microwave point cloud from 00 camera view under 0 state in scenario without background and with distractors
│   │   ├── xyzrgb_01.npz         # microwave point cloud from 01 camera view under 0 state in scenario without background and with distractors
│   │   └── ...
│   ├── 1_with_chaos
│   └── ...
├── refrigerator
└── ...

with_table
├── microwave
│   ├── 0_without_chaos
│   │   ├── xyzrgb_00.npz         # microwave point cloud from 00 camera view under 0 state in scenario with background and without distractors
│   │   ├── xyzrgb_01.npz         # microwave point cloud from 01 camera view under 0 state in scenario with background and without distractors
│   │   └── ...
│   ├── 1_without_chaos
│   ├── ...
│   ├── 0_with_chaos
│   │   ├── xyzrgb_00.npz         # microwave point cloud from 00 camera view under 0 state in scenario with background and with distractors
│   │   ├── xyzrgb_01.npz         # microwave point cloud from 01 camera view under 0 state in scenario with background and with distractors
│   │   └── ...
│   ├── 1_with_chaos
│   └── ...
├── refrigerator
└── ...
```

## Dataset Creation
All data are collected by a wrist-mounted Intel RealSense L515 LiDAR camera on a 7-DOF Franka Emika robot arm. The details of collection process are presented in the [paper](https://arxiv.org/abs/2403.16023).

## Dataset Load
```python
import numpy as np

data = np.load("./with_table/microwave/0_without_chaos/xyzrgb_00.npz")
xyz = data['point_cloud'].astype(np.float32)          # (N, 3)
rgb = data['rgb'].astype(np.float32)                  # (N, 3)
art = data['joints']                                  # (J, 10)
joint_origins = art[:, 0:3].astype(np.float32)        # (J, 3)
joint_directions = art[:, 3:6].astype(np.float32)     # (J, 3)
affordable_points = art[:, 6:9].astype(np.float32)    # (J, 3)
articulation_types = art[:, -1].astype(np.int64)      # (J,)
```

## Dataset License
This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. If you find it helpful, please consider citing our work: 
```
@article{wang2024rpmart,
  title={RPMArt: Towards Robust Perception and Manipulation for Articulated Objects},
  author={Wang, Junbo and Liu, Wenhai and Yu, Qiaojun and You, Yang and Liu, Liu and Wang, Weiming and Lu, Cewu},
  journal={arXiv preprint arXiv:2403.16023},
  year={2024}
}
```