| ---
|
| license: apache-2.0
|
| ---
|
|
|
| ## Latest Updates
|
| - [2025-10-23] v0.3 Dataset Released. 304 episodes across five tasks. This dataset was collected using two separate evaluation robot arms (one in Beijing and one in Shanghai). This can be understood as data from systems that have slight differences in their kinematic or sensor calibration parameters.
|
| - [2025-10-21] We have released the real-bot mcap data packaging tool (supporting both Arrow and LMDB formats). This tool can package **mcap files** recorded by the [Data Recorder Toolkit](https://github.com/HorizonRobotics/robo_orchard_data_recorder). Packer Source Code ref to [Code](https://github.com/HorizonRobotics/RoboOrchardLab/tree/master/robo_orchard_lab/dataset/horizon_manipulation/packer), Usage Instructions ref to [Docs](pack_demo/README.md)
|
| - [2025-10-14] v0.2 & v0.1 Dataset Updated: fixed the depth decode issue of the v0.2 dataset and added visualization for the v0.2 dataset.
|
| - [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. |
|
|
| ### Dataset Directory Layout |
| The dataset directories are organized by format, then by task, and finally by date-sorted parts: |
|
|
| ```text |
| lmdb_dataset/<task>/part-00000 |
| arrow_dataset/<task>/part-00000 |
| ``` |
|
|
| The `part-xxxxx` names are assigned in ascending collection-date order. For the same day, entries without a region suffix come first, followed by `bj` and then `sh`. |
|
|
| ### ⚠ 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 0.3
|
| <table>
|
| <thead>
|
| <tr>
|
| <th align="left">Task</th>
|
| <th align="center">Episode Num</th>
|
| <th align="left">LMDB Dataset</th>
|
| <th align="left">Arrow Dataset</th>
|
| </tr>
|
| </thead>
|
| <tbody>
|
| <tr>
|
| <td rowspan="2" align="left">place_shoe</td>
|
| <td align="center">30</td>
|
| <td align="left">lmdb_dataset/place_shoe/part-00003</td> |
| <td align="left">arrow_dataset/place_shoe/part-00003</td> |
| </tr>
|
| <tr>
|
| <td align="center">31</td>
|
| <td align="left">lmdb_dataset/place_shoe/part-00004</td> |
| <td align="left">arrow_dataset/place_shoe/part-00004</td> |
| </tr>
|
| <tr>
|
| <td rowspan="2" align="left">empty_cup_place</td>
|
| <td align="center">30</td>
|
| <td align="left">lmdb_dataset/empty_cup_place/part-00002</td> |
| <td align="left">arrow_dataset/empty_cup_place/part-00002</td> |
| </tr>
|
| <tr>
|
| <td align="center">31</td>
|
| <td align="left">lmdb_dataset/empty_cup_place/part-00003</td> |
| <td align="left">arrow_dataset/empty_cup_place/part-00003</td> |
| </tr>
|
| <tr>
|
| <td rowspan="2" align="left">put_bottles_dustbin</td>
|
| <td align="center">30</td>
|
| <td align="left">lmdb_dataset/put_bottles_dustbin/part-00003</td> |
| <td align="left">arrow_dataset/put_bottles_dustbin/part-00003</td> |
| </tr>
|
| <tr>
|
| <td align="center">31</td>
|
| <td align="left">lmdb_dataset/put_bottles_dustbin/part-00004</td> |
| <td align="left">arrow_dataset/put_bottles_dustbin/part-00004</td> |
| </tr>
|
| <tr>
|
| <td rowspan="2" align="left">stack_bowls_three</td>
|
| <td align="center">30</td>
|
| <td align="left">lmdb_dataset/stack_bowls_three/part-00004</td> |
| <td align="left">arrow_dataset/stack_bowls_three/part-00004</td> |
| </tr>
|
| <tr>
|
| <td align="center">30</td>
|
| <td align="left">lmdb_dataset/stack_bowls_three/part-00005</td> |
| <td align="left">arrow_dataset/stack_bowls_three/part-00005</td> |
| </tr>
|
| <tr>
|
| <td rowspan="2" align="left">stack_blocks_three</td>
|
| <td align="center">30</td>
|
| <td align="left">lmdb_dataset/stack_blocks_three/part-00003</td> |
| <td align="left">arrow_dataset/stack_blocks_three/part-00003</td> |
| </tr>
|
| <tr>
|
| <td align="center">31</td>
|
| <td align="left">lmdb_dataset/stack_blocks_three/part-00004</td> |
| <td align="left">arrow_dataset/stack_blocks_three/part-00004</td> |
| </tr>
|
| </tbody>
|
| </table>
|
|
|
| ## 1.2 Version 0.2
|
| | Task | Episode Num | LMDB Dataset | Arrow Dataset | Visualization |
|
| | :--------: | :-------: |:-------: | :-------: | :-------: |
|
| | place_shoe | 220 | lmdb_dataset/place_shoe/part-00002 | arrow_dataset/place_shoe/part-00002 | **** | |
| | empty_cup_place | 196 | lmdb_dataset/empty_cup_place/part-00001 | arrow_dataset/empty_cup_place/part-00001 | **** | |
| | put_bottles_dustbin | 199 | lmdb_dataset/put_bottles_dustbin/part-00002 | arrow_dataset/put_bottles_dustbin/part-00002 | **** | |
| | stack_bowls_three | 200 | lmdb_dataset/stack_bowls_three/part-00002<br>lmdb_dataset/stack_bowls_three/part-00003 | arrow_dataset/stack_bowls_three/part-00002<br>arrow_dataset/stack_bowls_three/part-00003 | **** | |
| | stack_blocks_three | 200 | lmdb_dataset/stack_blocks_three/part-00002 | arrow_dataset/stack_blocks_three/part-00002 | **** | |
|
|
| ## 1.3 Version 0.1
|
| | Task | Episode Num | LMDB Dataset | Arrow Dataset |
|
| | :--------: | :-------: |:-------: | :-------: |
|
| | place_shoe | 200 | lmdb_dataset/place_shoe/part-00000<br>lmdb_dataset/place_shoe/part-00001 | arrow_dataset/place_shoe/part-00000<br>arrow_dataset/place_shoe/part-00001 |
| | empty_cup_place | 200 | lmdb_dataset/empty_cup_place/part-00000 | arrow_dataset/empty_cup_place/part-00000 | |
| | put_bottles_dustbin | 200 | lmdb_dataset/put_bottles_dustbin/part-00000<br>lmdb_dataset/put_bottles_dustbin/part-00001 | arrow_dataset/put_bottles_dustbin/part-00000<br>arrow_dataset/put_bottles_dustbin/part-00001 |
| | stack_bowls_three | 219 | lmdb_dataset/stack_bowls_three/part-00000<br>lmdb_dataset/stack_bowls_three/part-00001 |arrow_dataset/stack_bowls_three/part-00000<br>arrow_dataset/stack_bowls_three/part-00001 |
| | stack_blocks_three | 267 | lmdb_dataset/stack_blocks_three/part-00000<br>lmdb_dataset/stack_blocks_three/part-00001 | arrow_dataset/stack_blocks_three/part-00000<br>arrow_dataset/stack_blocks_three/part-00001 | |
|
|
| # 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 [ROMultiRowDataset](https://github.com/HorizonRobotics/RoboOrchardLab/blob/master/robo_orchard_lab/dataset/robot/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/part-00000", |
| "data/arrow_dataset/place_shoe/part-00001", |
| ], |
| 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](./visualization/arrow_foxglove_layout.json) to visualize the mcap file. Refer to [here](https://foxglove.dev/examples) to get more visualization example. |
|
|
| # 3. Data Packer
|
|
|
| **Data Packaging** is the process of parsing the recorded data, performing operations like timestamp alignment, and converting it into a usable format for training.
|
|
|
| For **mcap data** recorded from real-bot, please refer to the released packaging script to perform the conversion.
|
|
|
| - **Source Code:** [Packer](https://github.com/HorizonRobotics/RoboOrchardLab/tree/master/robo_orchard_lab/dataset/horizon_manipulation/packer)
|
| - **Usage:** [Instructions](pack_demo/README.md) |
|
|
