|
|
---
|
|
|
license: apache-2.0
|
|
|
---
|
|
|
|
|
|
## Latest Updates
|
|
|
- [2025-09-24] 🚀 v0.2 Dataset Released: 1015 episodes across five tasks. Available in both Arrow and LMDB formats. The v0.2 dataset was created after we re-calibrated the zero point of our robotic arm.
|
|
|
- [2025-09-04] v0.1 Dataset Released: 1086 episodes across five tasks. Available in Arrow and LMDB formats. (See note on zero-point drift).
|
|
|
|
|
|
# 1. Data Introduction
|
|
|
### Data Format
|
|
|
This project provides robotic manipulation datasets in two formats: Arrow and LMDB:
|
|
|
- Arrow Dataset: Built on the [Apache Arrow](https://arrow.apache.org/) format. Its column-oriented structure offers flexibility and will be the primary format for development in robo_orchard_lab. It features standardized message types and supports exporting to Mcap files for visualization.
|
|
|
- LMDB Dataset: Built on the [LMDB](https://github.com/LMDB) (Lightning Memory-Mapped Database) format, which is optimized for extremely fast read speeds.
|
|
|
|
|
|
### ⚠ Important Note on Dataset Versions
|
|
|
|
|
|
The v0.1 dataset was affected by a robotic arm zero-point drift issue during data acquisition. We have since re-calibrated the arm and collected the v0.2 dataset.
|
|
|
- v0.2: Please use this version for all fine-tuning and evaluation to ensure model accuracy.
|
|
|
- v0.1: This version should only be used for pre-training experiments or deprecated entirely.
|
|
|
|
|
|
### Verifying Hardware Consistency
|
|
|
|
|
|
If you are using your own Piper robot arm, you can check for the same zero-point drift issue:
|
|
|
|
|
|
1. Check Hardware Zero Alignment: Home the robot arm and visually inspect if each joint aligns correctly with the physical zero-point markers.
|
|
|
2. Replay v0.2 Dataset: Replay the joint states from the v0.2 dataset. If the arm successfully completes the tasks, your hardware setup is consistent with ours.
|
|
|
|
|
|
## 1.1 Version 0.2
|
|
|
| Task | Episode Num | LMDB Dataset | Arrow Dataset |
|
|
|
| :--------: | :-------: |:-------: | :-------: |
|
|
|
| place_shoe | 220 | lmdb_dataset_place_shoe_2025_09_11 | arrow_dataset_place_shoe_2025_09_11
|
|
|
| empty_cup_place | 196 | lmdb_dataset_empty_cup_place_2025_09_09 | arrow_dataset_empty_cup_place_2025_09_09 |
|
|
|
| put_bottles_dustbin | 199 | lmdb_dataset_put_bottles_dustbin_2025_09_11 | lmdb_dataset_put_bottles_dustbin_2025_09_11
|
|
|
| stack_bowls_three | 200 | lmdb_dataset_stack_bowls_three_2025_09_09<br>lmdb_dataset_stack_bowls_three_2025_09_10 |arrow_dataset_stack_bowls_three_2025_09_09<br>arrow_dataset_stack_bowls_three_2025_09_10
|
|
|
| stack_blocks_three | 200 | lmdb_dataset_stack_blocks_three_2025_09_10 | arrow_dataset_stack_blocks_three_2025_09_10 |
|
|
|
|
|
|
|
|
|
## 1.2 Version 0.1
|
|
|
| Task | Episode Num | LMDB Dataset | Arrow Dataset |
|
|
|
| :--------: | :-------: |:-------: | :-------: |
|
|
|
| place_shoe | 200 | lmdb_dataset_place_shoe_2025_08_21<br>lmdb_dataset_place_shoe_2025_08_27 | arrow_dataset_place_shoe_2025_08_21<br>arrow_dataset_place_shoe_2025_08_27
|
|
|
| empty_cup_place | 200 | lmdb_dataset_empty_cup_place_2025_08_19 | arrow_dataset_empty_cup_place_2025_08_19 |
|
|
|
| put_bottles_dustbin | 200 | lmdb_dataset_put_bottles_dustbin_2025_08_20<br>lmdb_dataset_put_bottles_dustbin_2025_08_21 | arrow_dataset_put_bottles_dustbin_2025_08_20<br>arrow_dataset_put_bottles_dustbin_2025_08_21
|
|
|
| stack_bowls_three | 219 | lmdb_dataset_stack_bowls_three_2025_08_19<br>lmdb_dataset_stack_bowls_three_2025_08_20 |arrow_dataset_stack_bowls_three_2025_08_19<br>arrow_dataset_stack_bowls_three_2025_08_20
|
|
|
| stack_blocks_three | 267 | lmdb_dataset_stack_blocks_three_2025_08_26<br>lmdb_dataset_stack_blocks_three_2025_08_27 | arrow_dataset_stack_blocks_three_2025_08_26<br>arrow_dataset_stack_blocks_three_2025_08_27 |
|
|
|
|
|
|
# 2. Usage Example
|
|
|
|
|
|
## 2.1 LMDB Dataset Usage Example
|
|
|
|
|
|
Ref to [RoboTwinLmdbDataset](https://github.com/HorizonRobotics/robo_orchard_lab/blob/master/robo_orchard_lab/dataset/robotwin/robotwin_lmdb_dataset.py) class from robo_orchard_lab. See [SEM config](https://github.com/HorizonRobotics/robo_orchard_lab/blob/master/projects/sem/robotwin/config_sem_robotwin.py#L42) for a usage example.
|
|
|
|
|
|
|
|
|
## 2.2 Arrow Dataset Usage Example
|
|
|
|
|
|
Ref to [ManipulationRODataset](https://github.com/HorizonRobotics/robo_orchard_lab/blob/master/robo_orchard_lab/dataset/robotwin/arrow_dataset.py) class from robo_orchard_lab. Here is some usage example:
|
|
|
|
|
|
### 2.2.1 Data Parse Example
|
|
|
```python
|
|
|
def build_dataset(config):
|
|
|
from robo_orchard_lab.dataset.robot.dataset import (
|
|
|
ROMultiRowDataset,
|
|
|
ConcatRODataset,
|
|
|
)
|
|
|
from robo_orchard_lab.dataset.robotwin.transforms import ArrowDataParse
|
|
|
from robo_orchard_lab.dataset.robotwin.transforms import EpisodeSamplerConfig
|
|
|
|
|
|
dataset_list = []
|
|
|
data_parser = ArrowDataParse(
|
|
|
cam_names=config["cam_names"],
|
|
|
load_image=True,
|
|
|
load_depth=True,
|
|
|
load_extrinsic=True,
|
|
|
depth_scale=1000,
|
|
|
)
|
|
|
joint_sampler = EpisodeSamplerConfig(target_columns=["joints", "actions"])
|
|
|
|
|
|
for path in config["data_path"]:
|
|
|
dataset = ROMultiRowDataset(
|
|
|
dataset_path=path, row_sampler=joint_sampler
|
|
|
)
|
|
|
dataset.set_transform(data_parser)
|
|
|
dataset_list.append(dataset)
|
|
|
|
|
|
dataset = ConcatRODataset(dataset_list)
|
|
|
return dataset
|
|
|
|
|
|
config = dict(
|
|
|
data_path=[
|
|
|
"data/arrow_dataset_place_shoe_2025_08_21",
|
|
|
"data/arrow_dataset_place_shoe_2025_08_27",
|
|
|
],
|
|
|
cam_names=["left", "middle", "right"],
|
|
|
)
|
|
|
dataset = build_dataset(config)
|
|
|
|
|
|
# Show all key
|
|
|
frame_index = 0
|
|
|
print(len(dataset))
|
|
|
print(dataset[frame_index].keys())
|
|
|
|
|
|
# Show important key
|
|
|
for key in ['joint_state', 'master_joint_state', 'imgs', 'depths', 'intrinsic', 'T_world2cam']:
|
|
|
print(f"{key}, shape is {dataset[frame_index][key].shape}")
|
|
|
print(f"Instuction: {dataset[frame_index]['text']}")
|
|
|
print(f"Dataset index: {dataset[frame_index]['dataset_index']}")
|
|
|
|
|
|
# ----Output Demo----
|
|
|
# joint_state, shape is (322, 14)
|
|
|
# master_joint_state, shape is (322, 14)
|
|
|
# imgs, shape is (3, 360, 640, 3)
|
|
|
# depths, shape is (3, 360, 640)
|
|
|
# intrinsic, shape is (3, 4, 4)
|
|
|
# T_world2cam, shape is (3, 4, 4)
|
|
|
# Instuction: Use one arm to grab the shoe from the table and place it on the mat.
|
|
|
# Dataset index: 1
|
|
|
|
|
|
```
|
|
|
|
|
|
### 2.2.2 For Training
|
|
|
To integrate this dataset into the training pipeline, you will need to incorporate data transformations. Please follow the approach used in the [lmdb_dataset](https://github.com/HorizonRobotics/RoboOrchardLab/blob/master/projects/sem/robotwin/config_sem_robotwin.py) to add the transforms.
|
|
|
|
|
|
```python
|
|
|
from robo_orchard_lab.dataset.robotwin.transforms import ArrowDataParse
|
|
|
from robo_orchard_lab.utils.build import build
|
|
|
from robo_orchard_lab.utils.misc import as_sequence
|
|
|
from torchvision.transforms import Compose
|
|
|
train_transforms, val_transforms = build_transforms(config)
|
|
|
train_transforms = [build(x) for x in as_sequence(train_transforms)]
|
|
|
|
|
|
composed_train_transforms = Compose([data_parser] + train_transforms)
|
|
|
train_dataset.set_transform(composed_train_transforms)
|
|
|
```
|
|
|
### 2.2.3 Export mcap file and use foxglove to viz
|
|
|
```
|
|
|
def export_mcap(dataset, episode_index, target_path):
|
|
|
"""Export the specified episode to an MCAP file."""
|
|
|
from robo_orchard_lab.dataset.experimental.mcap.batch_encoder.camera import ( # noqa: E501
|
|
|
McapBatchFromBatchCameraDataEncodedConfig,
|
|
|
)
|
|
|
from robo_orchard_lab.dataset.experimental.mcap.batch_encoder.joint_state import ( # noqa: E501
|
|
|
McapBatchFromBatchJointStateConfig,
|
|
|
)
|
|
|
from robo_orchard_lab.dataset.experimental.mcap.writer import (
|
|
|
Dataset2Mcap,
|
|
|
McapBatchEncoderConfig,
|
|
|
)
|
|
|
|
|
|
dataset2mcap_cfg: dict[str, McapBatchEncoderConfig] = {
|
|
|
"joints": McapBatchFromBatchJointStateConfig(
|
|
|
target_topic="/observation/robot_state/joints"
|
|
|
),
|
|
|
}
|
|
|
dataset2mcap_cfg["actions"] = McapBatchFromBatchJointStateConfig(
|
|
|
target_topic="/action/robot_state/joints"
|
|
|
)
|
|
|
|
|
|
for camera_name in config["cam_names"]:
|
|
|
dataset2mcap_cfg[camera_name] = (
|
|
|
McapBatchFromBatchCameraDataEncodedConfig(
|
|
|
calib_topic=f"/observation/cameras/{camera_name}/calib",
|
|
|
image_topic=f"/observation/cameras/{camera_name}/image",
|
|
|
tf_topic=f"/observation/cameras/{camera_name}/tf",
|
|
|
)
|
|
|
)
|
|
|
dataset2mcap_cfg[f"{camera_name}_depth"] = (
|
|
|
McapBatchFromBatchCameraDataEncodedConfig(
|
|
|
image_topic=f"/observation/cameras/{camera_name}/depth",
|
|
|
)
|
|
|
)
|
|
|
|
|
|
to_mcap = Dataset2Mcap(dataset=dataset)
|
|
|
to_mcap.save_episode(
|
|
|
target_path=target_path,
|
|
|
episode_index=episode_index,
|
|
|
encoder_cfg=dataset2mcap_cfg,
|
|
|
)
|
|
|
print(f"Export episode {episode_index} to {target_path}")
|
|
|
|
|
|
|
|
|
# Export mcap file and use foxglove to viz
|
|
|
dataset_index = dataset[frame_index]["dataset_index"]
|
|
|
episode_index = dataset[frame_index]["episode"].index
|
|
|
export_mcap(
|
|
|
dataset=dataset.datasets[dataset_index],
|
|
|
episode_index=episode_index,
|
|
|
target_path=f"./viz_dataidx_{dataset_index}_episodeidx_{episode_index}.mcap",
|
|
|
)
|
|
|
```
|
|
|
|
|
|
Then you can use [Foxglove](https://foxglove.dev/) and [Example Layout](./arrow_foxglove_layout.json) to visualize the mcap file. Refer to [here](https://foxglove.dev/examples) to get more visualization example.
|
|
|
|
