README.md

# fastumi_test

## Description
FastUMI Pro robot manipulation dataset in RLDS format.

**Task**: pick up the object

## Dataset Info
- **Format**: RLDS (Reinforcement Learning Datasets)
- **Total Episodes**: 12
- **Total Steps**: 6692
- **FPS**: 30
- **Robot Type**: fastumi_pro
- **Action Type**: absolute
- **Image Encoding**: JPEG
- **Image Shape**: (1080, 1920, 3)

## Features

### Observation
| Feature | Shape | Description |
|---------|-------|-------------|
| `observation/image` | (1080, 1920, 3) | RGB camera image |
| `observation/state` | (7,) | End-effector pose: [x, y, z, roll, pitch, yaw, gripper] |

### Action
| Feature | Shape | Description |
|---------|-------|-------------|
| `action` | (7,) | Target pose (next state) |

### Standard RLDS Fields
| Feature | Type | Description |
|---------|------|-------------|
| `reward` | float | 1.0 at episode end, 0.0 otherwise |
| `discount` | float | 0.0 at episode end, 1.0 otherwise |
| `is_first` | bool | True for first step |
| `is_last` | bool | True for last step |
| `is_terminal` | bool | False (demonstrations are successful) |
| `language_instruction` | string | Task description |

## Loading the Dataset

```python
import tensorflow as tf

# Load TFRecords
dataset = tf.data.TFRecordDataset([
    'fastumi_test/1.0.0/fastumi_test-train.tfrecord-00000-of-00001'
])

# Parse function
def parse_episode(serialized):
    features = {
        'episode_id': tf.io.FixedLenFeature([], tf.string),
        'num_steps': tf.io.FixedLenFeature([], tf.int64),
        'steps/observation/image': tf.io.VarLenFeature(tf.string),
        'steps/observation/state': tf.io.VarLenFeature(tf.float32),
        'steps/action': tf.io.VarLenFeature(tf.float32),
        'steps/reward': tf.io.VarLenFeature(tf.float32),
        'steps/is_first': tf.io.VarLenFeature(tf.int64),
        'steps/is_last': tf.io.VarLenFeature(tf.int64),
        'steps/language_instruction': tf.io.VarLenFeature(tf.string),
    }
    return tf.io.parse_single_example(serialized, features)

dataset = dataset.map(parse_episode)
```

## Data Source Mapping

### Source Files (per session)
```
session_YYYYMMDD_HHMMSS/
├── SLAM_Poses/
│   └── slam_raw_baseframe.txt    -> observation/state, action
├── RGB_Images/
│   ├── video.mp4                 -> observation/image
│   └── timestamps.csv            -> temporal alignment
└── Clamp_Data/
    └── clamp_data_tum.txt        (gripper data included in SLAM file)
```

## Citation

If you use this dataset, please cite:

```bibtex
@misc{fastumi_rlds,
    title={FastUMI Pro RLDS Dataset},
    year={2024},
}
```