README.md

---
license: mit
language:
- en
---
# Dataset Card for TRANSIC Data

This dataset card is accompanied with the [CoRL 2024 paper](https://arxiv.org/abs/2405.10315) titled TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction.
It includes generated simulation data and real-robot human correction data for sim-to-real transfer of robotic arm manipulation policies.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
This dataset includes two parts, 1) simulation data used in student policy distillation and 2) real-robot data used in residual policy learning.

The first part can be found in the `distillation` folder. We include 5 tasks in the `distillation/tasks` directory. For each task, we provide 10,000 successful trajectories generated by teacher policies trained with reinforcement learning in simulation.
Furthermore, we also provide `matched_point_cloud_scenes.h5`, a seperate collection of 59 matched point clouds in simulation and the real world. We use them to regularize the point-cloud encoder during policy training.

The second part can be found in the `correction_data` folder. We include real-world human correction data for 5 tasks. Each task contains different number of trajectories. Each trajectory includes observations, pre-intervention actions, and post-intervention actions for residual policy learning.

- **Curated by:** [Yunfan Jiang](https://yunfanj.com/)
- **License:** [MIT](LICENSE)

### Dataset Sources

- **Repositories:** [TRANSIC](https://github.com/transic-robot/transic), [TRANSIC-Envs](https://github.com/transic-robot/transic-envs)
- **Paper:** [TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction](https://arxiv.org/abs/2405.10315)

## Uses

Please see our [codebase](https://github.com/transic-robot/transic) for detailed usage.

## Dataset Structure

Structure for `distillation/tasks/*.hdf5`:
```
data[f"rollouts/successful/rollout_{idx}/actions"]: shape (L, 7), first 6 dimensions represent end-effector's pose change. The last dimension corresponds to the gripper action.
data[f"rollouts/successful/rollout_{idx}/eef_pos"]: shape (L + 1, 3), end-effector's positions.
data[f"rollouts/successful/rollout_{idx}/eef_quat"]: shape (L + 1, 4), end-effector's orientations in quaternion.
data[f"rollouts/successful/rollout_{idx}/franka_base"]: shape (L + 1, 7), robot base pose.
data[f"rollouts/successful/rollout_{idx}/gripper_width"]: shape (L + 1, 1), gripper's current width.
data[f"rollouts/successful/rollout_{idx}/leftfinger"]: shape (L + 1, 7), left gripper finger pose.
data[f"rollouts/successful/rollout_{idx}/q"]: shape (L + 1, 7), robot joint positions.
data[f"rollouts/successful/rollout_{idx}/rightfinger"]: shape (L + 1, 7), right gripper finger pose.
data[f"rollouts/successful/rollout_{idx}/{obj}"]: shape (L + 1, 7), pose for each object.
```

Structure for `distillation/matched_point_cloud_scenes.h5`:
```
# sim
data[f"{date}/{idx}/sim/ee_mask"]: shape (N,), represent if each point in the point cloud corresponds to the end-effector. 0: not end-effector, 1: end-effector.
data[f"{date}/{idx}/sim/franka_base"]: shape (7,), robot base pose.
data[f"{date}/{idx}/sim/leftfinger"]: shape (7,), left gripper finger pose.
data[f"{date}/{idx}/sim/pointcloud"]: shape (N, 3), synthetic point cloud.
data[f"{date}/{idx}/sim/q"]: shape (9,), robot joint positions, last two dimensions correspond to two gripper fingers.
data[f"{date}/{idx}/sim/rightfinger"]: shape (7,), right gripper finger pose.
data[f"{date}/{idx}/sim/{obj}"]: shape (7,), pose for each object.

# real
data[f"{date}/{idx}/real/{sample}/eef_pos"]: shape (3, 1), end-effector's position.
data[f"{date}/{idx}/real/{sample}/eef_quat"]: shape (4), end-effector's orientations in quaternion.
data[f"{date}/{idx}/real/{sample}/fk_finger_pointcloud"]: shape (N, 3), point cloud for gripper fingers obtained through forward kinematics.
data[f"{date}/{idx}/real/{sample}/gripper_width"]: shape (), gripper width.
data[f"{date}/{idx}/real/{sample}/measured_pointcloud"]: shape (N, 3), point cloud captured by depth cameras.
data[f"{date}/{idx}/real/{sample}/q"]: shape (7,), robot joint positions.
```

Structure for `correction_data/*/*.hdf5`:
```
data["is_human_intervention"]: shape (L,), represent human intervention (1) or not (0).
data["policy_action"]: shape (L, 8), simulation policies' actions.
data["policy_obs"]: shape (L, ...), simulation policies' observations.
data["post_intervention_eef_pose"]: shape (L, 4, 4), end-effector's pose after intervention.
data["post_intervention_q"]: shape (L, 7), robot joint positions after intervention.
data["post_intervention_gripper_q"]: shape (L, 2), gripper fingers' positions after intervention.
data["pre_intervention_eef_pose"]: shape (L, 4, 4), end-effector's pose before intervention.
data["pre_intervention_q"]: shape (L, 7), robot joint positions before intervention.
data["pre_intervention_gripper_q"]: shape (L, 2), gripper fingers' positions before intervention.
```

## Dataset Creation
`distillation/tasks/*.hdf5` are generated by teacher policies trained with reinforcement learning in simulation. 
`distillation/matched_point_cloud_scenes.h5` and `correction_data/*/*.hdf5` are manually collected in the real world.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@inproceedings{jiang2024transic,
  title     = {TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction},
  author    = {Yunfan Jiang and Chen Wang and Ruohan Zhang and Jiajun Wu and Li Fei-Fei},
  booktitle = {Conference on Robot Learning},
  year      = {2024}
}
```

## Dataset Card Contact

[Yunfan Jiang](https://yunfanj.com/), email: `yunfanj[at]cs[dot]stanford[dot]edu`