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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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LICENSE

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+---
+title: MIT License
+spdx-id: MIT
+featured: true
+hidden: false
+
+description: A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
+
+how: Create a text file (typically named LICENSE or LICENSE.txt) in the root of your source code and copy the text of the license into the file. Replace [year] with the current year and [fullname] with the name (or names) of the copyright holders.
+
+using:
+  Babel: https://github.com/babel/babel/blob/master/LICENSE
+  .NET: https://github.com/dotnet/runtime/blob/main/LICENSE.TXT
+  Rails: https://github.com/rails/rails/blob/master/MIT-LICENSE
+
+permissions:
+  - commercial-use
+  - modifications
+  - distribution
+  - private-use
+
+conditions:
+  - include-copyright
+
+limitations:
+  - liability
+  - warranty
+
+---
+
+MIT License
+
+Copyright (c) 2024 Yunfan Jiang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+language:
+- en
+---
+# Dataset Card for TRANSIC Data
+
+This dataset card is accompanied with the [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:**
+
+```
+@article{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},
+  year    = {2024},
+  journal = {arXiv preprint arXiv: Arxiv-2405.10315}
+}
+```
+
+## Dataset Card Contact
+
+[Yunfan Jiang](https://yunfanj.com/), email: `yunfanj[at]cs[dot]stanford[dot]edu`

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