episode_id dict | num_steps dict | steps dict |
|---|---|---|
{
"dtype": "string",
"shape": []
} | {
"dtype": "int64",
"shape": []
} | {
"observation": {
"image": {
"dtype": "uint8",
"shape": [
1080,
1920,
3
],
"encoding": "JPEG"
},
"state": {
"dtype": "float32",
"shape": [
7
],
"description": "End-effector pose: [x, y, z, roll, pitch, yaw, gripper]"
}
... |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
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
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:
@misc{fastumi_rlds,
title={FastUMI Pro RLDS Dataset},
year={2024},
}
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