Agilex_Cobot_Magic_move_object_black_tablecloth

# Agilex_Cobot_Magic_move_object_black_tablecloth ## Dataset Description This dataset uses an extended format based on LeRobot and is fully compatible with LeRobot. ## Task Preview <video src="videos/chunk-000/observation.images.cam_head_rgb/episode_000000.mp4" controls width="640"></video> [View Video Directly](videos/chunk-000/observation.images.cam_head_rgb/episode_000000.mp4) ### Overview - **Total Episodes:** 200 - **Total Frames:** 119721 - **FPS:** 30 - **Dataset Size:** 6.75 GB - **Robot Name:** `Agilex_Cobot_Magic` - **End-Effector Type:** `two_finger_gripper` - **Teleoperation Type:** `Due to some reasons, this dataset temporarily cannot provide the teleoperation type information.` - **Sensors:** `cam_head_rgb`, `cam_left_wrist_rgb`, `cam_right_wrist_rgb` - **Camera Information:** cam_head_rgb; cam_left_wrist_rgb; cam_right_wrist_rgb - **Scene:** `commercial & convenience->supermarket` - **Objects:** `table(unknown)`, `black_table_cloths(unknown)`, `waffle(unknown)`, `green_lemon(unknown)`, `eggplant(unknown)`, `chewing_gum(unknown)`, `chocolate(unknown)`, `mango(unknown)`, `chewing_gum(unknown)`, `mint_candy(unknown)`, `mangosteen(unknown)`, `orange(unknown)`, `bread(unknown)`, `banana(unknown)`, `cake(unknown)`, `beef_cheeseburger(unknown)`, `bowl(unknown)`, `pan(unknown)`, `small_teapot(unknown)`, `small_teacup(unknown)`, `paper_ball(unknown)`, `brown_square_towel(unknown)`, `black_cylindrical_pen_holder(unknown)`, `pink_long_towel(unknown)`, `whiteboard_eraser(unknown)`, `mentholatum_facial_cleanser(unknown)`, `duck(unknown)`, `compass(unknown)`, `bowl(unknown)`, `blue_long_towel(unknown)` - **Task Description:** the gripper move the object. ### Primary Task Instruction > the gripper move the object. ### Robot Configuration - **Robot Name:** `Agilex_Cobot_Magic` - **Codebase Version:** `v2.1` - **End-Effector Type:** `two_finger_gripper` - **Teleoperation Type:** `Due to some reasons, this dataset temporarily cannot provide the teleoperation type information.` ## Scene and Objects ### Scene Type `commercial & convenience->supermarket` ### Objects - `table(unknown)` - `black_table_cloths(unknown)` - `waffle(unknown)` - `green_lemon(unknown)` - `eggplant(unknown)` - `chewing_gum(unknown)` - `chocolate(unknown)` - `mango(unknown)` - `chewing_gum(unknown)` - `mint_candy(unknown)` - `mangosteen(unknown)` - `orange(unknown)` - `bread(unknown)` - `banana(unknown)` - `cake(unknown)` - `beef_cheeseburger(unknown)` - `bowl(unknown)` - `pan(unknown)` - `small_teapot(unknown)` - `small_teacup(unknown)` - `paper_ball(unknown)` - `brown_square_towel(unknown)` - `black_cylindrical_pen_holder(unknown)` - `pink_long_towel(unknown)` - `whiteboard_eraser(unknown)` - `mentholatum_facial_cleanser(unknown)` - `duck(unknown)` - `compass(unknown)` - `bowl(unknown)` - `blue_long_towel(unknown)` ## Task Descriptions - **Standardized Task Description:** `the gripper move the object.` - **Operation Type:** `Due to some reasons, this dataset temporarily cannot provide the operation type information.` - **Environment Type:** `Due to some reasons, this dataset temporarily cannot provide the environment type information.` ### Sub-Tasks This dataset includes 141 distinct subtasks: 1. **Place the XX on the table with the left gripper** (Index: 0) 2. **Grasp the pen container with the right gripper ** (Index: 1) 3. **Grasp the blue bowl with the left gripper ** (Index: 2) 4. **Grasp the pen container with the right gripper** (Index: 3) 5. **Place the square chewing gum on the table with the left gripper ** (Index: 4) 6. **Grasp the pink towel with the left gripper ** (Index: 5) 7. **Place the blue pot on the table with the right gripper ** (Index: 6) 8. **Place the brown towel on the table with the left gripper ** (Index: 7) 9. **Place the waffle on the table with the left gripper ** (Index: 8) 10. **Place the white duck on the table with the right gripper ** (Index: 9) 11. **Place the white blackboard erasure on the table with the left gripper ** (Index: 10) 12. **Grasp the compass with the left gripper ** (Index: 11) 13. **Grasp the green lemon with the right gripper ** (Index: 12) 14. **Grasp the cyan cup with the left gripper ** (Index: 13) 15. **Place the blue bowl on the table with the left gripper ** (Index: 14) 16. **Grasp the mint candy with the right gripper ** (Index: 15) 17. **Grasp the mint candy with the left gripper** (Index: 16) 18. **Grasp the cyan cup with the right gripper ** (Index: 17) 19. **Grasp the green lemon with the left gripper ** (Index: 18) 20. **Grasp the white blackboard erasure with the left gripper ** (Index: 19) 21. **Grasp the white duck with the left gripper ** (Index: 20) 22. **Grasp the square chewing gum with the left gripper** (Index: 21) 23. **Grasp the mint candy with the left gripper ** (Index: 22) 24. **Place the brown towel on the table with the left gripper** (Index: 23) 25. **Place the on the table with the right gripper ** (Index: 24) 26. **Grasp the XX with the right gripper** (Index: 25) 27. **Place the blue blackboard erasure on the table with the left gripper ** (Index: 26) 28. **Place the white blackboard erasure on the table with the left gripper** (Index: 27) 29. **Place the teapot on the table with the right gripper ** (Index: 28) 30. **Grasp the white blackboard erasure with the left gripper** (Index: 29) 31. **Grasp the Square chewing gum with the right gripper ** (Index: 30) 32. **Place the pink towel on the table with the left gripper ** (Index: 31) 33. **Grasp the hard facial cleanser with the right gripper ** (Index: 32) 34. **Grasp the with the left gripper ** (Index: 33) 35. **Place the hard facial cleanser on the table with the right gripper** (Index: 34) 36. **Grasp the white duck with the left gripper ** (Index: 35) 37. **Place the mint candy on the table with the right gripper** (Index: 36) 38. **Grasp the brown towel with the left gripper** (Index: 37) 39. **Grasp the chocolate with the right gripper** (Index: 38) 40. **Grasp the brown towel with the right gripper** (Index: 39) 41. **Grasp the mango with the left gripper** (Index: 40) 42. **Place the white blackboard erasure on the table with the left gripper ** (Index: 41) 43. **Grasp the brown towel with the left gripper ** (Index: 42) 44. **Place the mango on the table with the right gripper ** (Index: 43) 45. **Place the brown towel on the table with the left gripper ** (Index: 44) 46. **Place the teapot on the table with the left gripper** (Index: 45) 47. **Grasp the hard facial cleanser with the right gripper** (Index: 46) 48. **Place the pink towel on the table with the right gripper ** (Index: 47) 49. **Grasp the pink towel with the right gripper ** (Index: 48) 50. **Place the waffle on the table with the right gripper** (Index: 49) 51. **Place the blue pot on the table with the left gripper ** (Index: 50) 52. **Place the pen container on the table with the right gripper** (Index: 51) 53. **Grasp the blue pot with the right gripper ** (Index: 52) 54. **Grasp the pen container with the left gripper ** (Index: 53) 55. **Grasp the green lemon with the right gripper ** (Index: 54) 56. **Grasp the eggplant with the right gripper** (Index: 55) 57. **Place the white blackboard erasure on the table with the left gripper ** (Index: 56) 58. **Place the eggplant on the table with the left gripper ** (Index: 57) 59. **Place the green lemon on the table with the left gripper** (Index: 58) 60. **Place the XX on the table with the right gripper** (Index: 59) 61. **End** (Index: 60) 62. **Grasp the white blackboard erasure with the right gripper** (Index: 61) 63. **Place the white duck on the table with the left gripper ** (Index: 62) 64. **Place the orange on the table with the left gripper ** (Index: 63) 65. **Grasp the eggplant with the right gripper ** (Index: 64) 66. **Grasp the brown towel with the right gripper ** (Index: 65) 67. **Place the square chewing gum on the table with the right gripper ** (Index: 66) 68. **Place the compass on the table with the right gripper ** (Index: 67) 69. **Grasp the orange with the left gripper ** (Index: 68) 70. **Place the hard facial cleanser on the table with the left gripper ** (Index: 69) 71. **Grasp the blue blackboard erasure with the left gripper ** (Index: 70) 72. **Place the brown towel on the table with the right gripper ** (Index: 71) 73. **Grasp the blue blackboard erasure with the right gripper ** (Index: 72) 74. **Grasp the eggplant with the left gripper ** (Index: 73) 75. **Grasp the square chewing gum with the right gripper ** (Index: 74) 76. **Place the mango on the table with the left gripper ** (Index: 75) 77. **Grasp the waffle with the right gripper** (Index: 76) 78. **Grasp the white blackboard erasure with the right gripper ** (Index: 77) 79. **Place the teapot on the table with the left gripper ** (Index: 78) 80. **Place the waffle on the table with the right gripper ** (Index: 79) 81. **Grasp the mango with the right gripper ** (Index: 80) 82. **Grasp the white blackboard erasure with the left gripper ** (Index: 81) 83. **Grasp the blue pot with the left gripper ** (Index: 82) 84. **Place the square chewing gum on the table with the right gripper** (Index: 83) 85. **Place the pen container on the table with the right gripper ** (Index: 84) 86. **Place the green lemon on the table with the right gripper ** (Index: 85) 87. **Place the blue blackboard erasure on the table with the right gripper ** (Index: 86) 88. **Grasp the teapot with the left gripper ** (Index: 87) 89. **Place the borwn towel on the table with the right gripper ** (Index: 88) 90. **Place the pen container on the table with the left gripper ** (Index: 89) 91. **Grasp the compass with the left gripper ** (Index: 90) 92. **Grasp the fruit candy with the right gripper ** (Index: 91) 93. **Place the cyan cup on the table with the left gripper ** (Index: 92) 94. **Place the fruit candy on the table with the right gripper ** (Index: 93) 95. **Place the compass on the table with the left gripper ** (Index: 94) 96. **Grasp the white duck with the right gripper ** (Index: 95) 97. **Grasp the waffle with the right gripper ** (Index: 96) 98. **Place the blue pot on the table with the left gripper ** (Index: 97) 99. **Grasp the mango with the left gripper ** (Index: 98) 100. **Grasp the teapot with the right gripper ** (Index: 99) 101. **Place the teacup on the table with the left gripper ** (Index: 100) 102. **Grasp the pink towel with the right gripper ** (Index: 101) 103. **Grasp the blue pot with the left gripper ** (Index: 102) 104. **Place the mango on the table with the right gripper ** (Index: 103) 105. **Place the mangosteen on the table with the left gripper** (Index: 104) 106. **Grasp the square chewing gum with the left gripper ** (Index: 105) 107. **Grasp the square chewing gum with the right gripper** (Index: 106) 108. **Grasp the compass with the right gripper ** (Index: 107) 109. **Place the tea cup on the table with the right gripper ** (Index: 108) 110. **Place the teapot on the table with the left gripper ** (Index: 109) 111. **Grasp the cyan cup with the left gripper ** (Index: 110) 112. **Grasp the eggplant with the right gripper ** (Index: 111) 113. **Place the white blackboard erasure on the table with the right gripper** (Index: 112) 114. **Place the green lemon on the table with the left gripper ** (Index: 113) 115. **Grasp the hard facial cleanser with the left gripper ** (Index: 114) 116. **Place the white blackboard erasure on the table with the right gripper ** (Index: 115) 117. **Place the mango on the table with the left gripper** (Index: 116) 118. **Grasp the tea cup with the right gripper ** (Index: 117) 119. **Grasp the waffle with the left gripper ** (Index: 118) 120. **Grasp the pink towel with the left gripper ** (Index: 119) 121. **Place the hard facial cleanser on the table with the right gripper ** (Index: 120) 122. **Place the eggplant on the table with the right gripper ** (Index: 121) 123. **Place the mint candy on the table with the left gripper ** (Index: 122) 124. **Grasp the mint candy with the right gripper ** (Index: 123) 125. **Grasp the chocolate with the right gripper ** (Index: 124) 126. **Place the cyan cup on the table with the right gripper ** (Index: 125) 127. **Grasp the XX with the left gripper** (Index: 126) 128. **Place the chocolate on the table with the right gripper** (Index: 127) 129. **Grasp the mint candy with the right gripper** (Index: 128) 130. **Grasp the with the right gripper ** (Index: 129) 131. **Place the brown towel on the table with the right gripper** (Index: 130) 132. **Grasp the green lemon with the left gripper** (Index: 131) 133. **Place the Mangosteen on the table with the right gripper** (Index: 132) 134. **Place the square chewing gum on the table with the left gripper** (Index: 133) 135. **Grasp the teacup with the left gripper ** (Index: 134) 136. **Place the chocolate on the table with the right gripper ** (Index: 135) 137. **Grasp the blue bowl with the left gripper ** (Index: 136) 138. **Grasp the teapot with the left gripper** (Index: 137) 139. **Place the pink bowel on the table with the right gripper ** (Index: 138) 140. **Place the mint candy on the table with the right gripper ** (Index: 139) 141. **null** (Index: 140) ### Atomic Actions - `grasp` - `lift` - `lower` ## Hardware and Sensors ### Sensors - `cam_head_rgb` - `cam_left_wrist_rgb` - `cam_right_wrist_rgb` ### Camera Information - `cam_head_rgb`: dtype=video, shape=480x640x3, resolution=640x480, codec=av1, pix_fmt=yuv420p - `cam_left_wrist_rgb`: dtype=video, shape=480x640x3, resolution=640x480, codec=av1, pix_fmt=yuv420p - `cam_right_wrist_rgb`: dtype=video, shape=480x640x3, resolution=640x480, codec=av1, pix_fmt=yuv420p ### Coordinate System - **Definition:** `right-hand-frame` ### Dimensions & Units - **Joint Rotation:** `radian` - **End-Effector Rotation:** `radian` - **End-Effector Translation:** `meter` ## Dataset Statistics | Metric | Value | |--------|-------| | **Total Episodes** | 200 | | **Total Frames** | 119721 | | **Total Tasks** | 141 | | **Total Videos** | 600 | | **Total Chunks** | 1 | | **Chunk Size** | 1000 | | **FPS** | 30 | | **State Dimensions** | 26 | | **Action Dimensions** | 26 | | **Camera Views** | 3 | | **Dataset Size** | 6.75 GB | ## Data Splits The dataset is organized into the following splits: - **Training**: Episodes 0:199 ## Dataset Structure This dataset follows the LeRobot format and contains the following components: ### Data Files - **Videos**: Compressed video files containing RGB camera observations - **State Data**: Robot joint positions, velocities, and other state information - **Action Data**: Robot action commands and trajectories - **Metadata**: Episode metadata, timestamps, and annotations ### File Organization - **Data Path Pattern**: `data/chunk-{id}/episode_{id}.parquet` - **Video Path Pattern**: `videos/chunk-{id}/observation.images.cam_left_wrist_rgb/episode_{id}.mp{id}` - **Chunking**: Data is organized into 1 chunk(s) of size 1000 ### Data Structure (Tree) ``` Agilex_Cobot_Magic_move_object_black_tablecloth_qced_hardlink/ |-- annotations | |-- eef_acc_mag_annotation.jsonl | |-- eef_direction_annotation.jsonl | |-- eef_velocity_annotation.jsonl | |-- gripper_activity_annotation.jsonl | |-- gripper_mode_annotation.jsonl | |-- scene_annotations.jsonl | `-- subtask_annotations.jsonl |-- data | `-- chunk-000 | |-- episode_000000.parquet | |-- episode_000001.parquet | |-- episode_000002.parquet | |-- episode_000003.parquet | |-- episode_000004.parquet | |-- episode_000005.parquet | |-- episode_000006.parquet | |-- episode_000007.parquet | |-- episode_000008.parquet | |-- episode_000009.parquet | |-- episode_000010.parquet | `-- episode_000011.parquet | `-- ... (188 more entries) |-- meta | |-- episodes.jsonl | |-- episodes_stats.jsonl | |-- info.json | `-- tasks.jsonl |-- videos | `-- chunk-000 | |-- observation.images.cam_head_rgb | |-- observation.images.cam_left_wrist_rgb | `-- observation.images.cam_right_wrist_rgb |-- info.yaml `-- README.md ``` ## Camera Views This dataset includes 3 camera views: `cam_head_rgb`, `cam_left_wrist_rgb`, `cam_right_wrist_rgb`. ## Features (Full YAML) ```yaml observation.images.cam_head_rgb: dtype: video shape: - 480 - 640 - 3 names: - height - width - channels info: video.height: 480 video.width: 640 video.codec: av1 video.pix_fmt: yuv420p video.is_depth_map: false video.fps: 30 video.channels: 3 has_audio: false observation.images.cam_left_wrist_rgb: dtype: video shape: - 480 - 640 - 3 names: - height - width - channels info: video.height: 480 video.width: 640 video.codec: av1 video.pix_fmt: yuv420p video.is_depth_map: false video.fps: 30 video.channels: 3 has_audio: false observation.images.cam_right_wrist_rgb: dtype: video shape: - 480 - 640 - 3 names: - height - width - channels info: video.height: 480 video.width: 640 video.codec: av1 video.pix_fmt: yuv420p video.is_depth_map: false video.fps: 30 video.channels: 3 has_audio: false observation.state: dtype: float32 shape: - 26 names: - left_arm_joint_1_rad - left_arm_joint_2_rad - left_arm_joint_3_rad - left_arm_joint_4_rad - left_arm_joint_5_rad - left_arm_joint_6_rad - left_gripper_open - left_eef_pos_x_m - left_eef_pos_y_m - left_eef_pos_z_m - left_eef_rot_euler_x_rad - left_eef_rot_euler_y_rad - left_eef_rot_euler_z_rad - right_arm_joint_1_rad - right_arm_joint_2_rad - right_arm_joint_3_rad - right_arm_joint_4_rad - right_arm_joint_5_rad - right_arm_joint_6_rad - right_gripper_open - right_eef_pos_x_m - right_eef_pos_y_m - right_eef_pos_z_m - right_eef_rot_euler_x_rad - right_eef_rot_euler_y_rad - right_eef_rot_euler_z_rad action: dtype: float32 shape: - 26 names: - left_arm_joint_1_rad - left_arm_joint_2_rad - left_arm_joint_3_rad - left_arm_joint_4_rad - left_arm_joint_5_rad - left_arm_joint_6_rad - left_gripper_open - left_eef_pos_x_m - left_eef_pos_y_m - left_eef_pos_z_m - left_eef_rot_euler_x_rad - left_eef_rot_euler_y_rad - left_eef_rot_euler_z_rad - right_arm_joint_1_rad - right_arm_joint_2_rad - right_arm_joint_3_rad - right_arm_joint_4_rad - right_arm_joint_5_rad - right_arm_joint_6_rad - right_gripper_open - right_eef_pos_x_m - right_eef_pos_y_m - right_eef_pos_z_m - right_eef_rot_euler_x_rad - right_eef_rot_euler_y_rad - right_eef_rot_euler_z_rad timestamp: dtype: float32 shape: - 1 names: null frame_index: dtype: int64 shape: - 1 names: null episode_index: dtype: int64 shape: - 1 names: null index: dtype: int64 shape: - 1 names: null task_index: dtype: int64 shape: - 1 names: null subtask_annotation: names: null dtype: int32 shape: - 5 scene_annotation: names: null dtype: int32 shape: - 1 eef_sim_pose_state: names: - left_eef_pos_x - left_eef_pos_y - left_eef_pos_z - left_eef_rot_x - left_eef_rot_y - left_eef_rot_z - right_eef_pos_x - right_eef_pos_y - right_eef_pos_z - right_eef_rot_x - right_eef_rot_y - right_eef_rot_z dtype: float32 shape: - 12 eef_sim_pose_action: names: - left_eef_pos_x - left_eef_pos_y - left_eef_pos_z - left_eef_rot_x - left_eef_rot_y - left_eef_rot_z - right_eef_pos_x - right_eef_pos_y - right_eef_pos_z - right_eef_rot_x - right_eef_rot_y - right_eef_rot_z dtype: float32 shape: - 12 eef_direction_state: names: - left_eef_direction - right_eef_direction dtype: int32 shape: - 2 eef_direction_action: names: - left_eef_direction - right_eef_direction dtype: int32 shape: - 2 eef_velocity_state: names: - left_eef_velocity - right_eef_velocity dtype: int32 shape: - 2 eef_velocity_action: names: - left_eef_velocity - right_eef_velocity dtype: int32 shape: - 2 eef_acc_mag_state: names: - left_eef_acc_mag - right_eef_acc_mag dtype: int32 shape: - 2 eef_acc_mag_action: names: - left_eef_acc_mag - right_eef_acc_mag dtype: int32 shape: - 2 gripper_mode_state: names: - left_gripper_mode - right_gripper_mode dtype: int32 shape: - 2 gripper_mode_action: names: - left_gripper_mode - right_gripper_mode dtype: int32 shape: - 2 gripper_activity_state: names: - left_gripper_activity - right_gripper_activity dtype: int32 shape: - 2 gripper_activity_action: names: - left_gripper_activity - right_gripper_activity dtype: int32 shape: - 2 gripper_open_scale_state: names: - left_gripper_open_scale - right_gripper_open_scale dtype: float32 shape: - 2 gripper_open_scale_action: names: - left_gripper_open_scale - right_gripper_open_scale dtype: float32 shape: - 2 ``` ## Available Annotations This dataset includes rich annotations to support diverse learning approaches: - `eef_acc_mag_annotation.jsonl` - `eef_direction_annotation.jsonl` - `eef_velocity_annotation.jsonl` - `gripper_activity_annotation.jsonl` - `gripper_mode_annotation.jsonl` - `scene_annotations.jsonl` - `subtask_annotations.jsonl` ## Dataset Tags - `RoboCOIN` - `LeRobot` ## Authors ### Contributors This dataset is contributed by:-RoboCOIN Team at Beijing Academy of Artificial Intelligence (BAAI) ### Annotators No annotator information available. ## Links - **Homepage:** [https://flagopen.github.io/RoboCOIN/](https://flagopen.github.io/RoboCOIN/) - **Paper:** [https://arxiv.org/abs/2511.17441](https://arxiv.org/abs/2511.17441) - **Repository:** [https://github.com/FlagOpen/RoboCOIN](https://github.com/FlagOpen/RoboCOIN) ## Contact and Support For questions, issues, or feedback regarding this dataset, please contact us. ### Support For technical support, please open an issue on our GitHub repository. ## License apache-2.0 ## Citation If you use this dataset in your research, please cite: ```bibtex @article{robocoin, title={RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation}, author={Shihan Wu, Xuecheng Liu, Shaoxuan Xie, Pengwei Wang, Xinghang Li, Bowen Yang, Zhe Li, Kai Zhu, Hongyu Wu, Yiheng Liu, Zhaoye Long, Yue Wang, Chong Liu, Dihan Wang, Ziqiang Ni, Xiang Yang, You Liu, Ruoxuan Feng, Runtian Xu, Lei Zhang, Denghang Huang, Chenghao Jin, Anlan Yin, Xinlong Wang, Zhenguo Sun, Junkai Zhao, Mengfei Du, Mingyu Cao, Xiansheng Chen, Hongyang Cheng, Xiaojie Zhang, Yankai Fu, Ning Chen, Cheng Chi, Sixiang Chen, Huaihai Lyu, Xiaoshuai Hao, Yequan Wang, Bo Lei, Dong Liu, Xi Yang, Yance Jiao, Tengfei Pan, Yunyan Zhang, Songjing Wang, Ziqian Zhang, Xu Liu, Ji Zhang, Caowei Meng, Zhizheng Zhang, Jiyang Gao, Song Wang, Xiaokun Leng, Zhiqiang Xie, Zhenzhen Zhou, Peng Huang, Wu Yang, Yandong Guo, Yichao Zhu, Suibing Zheng, Hao Cheng, Xinmin Ding, Yang Yue, Huanqian Wang, Chi Chen, Jingrui Pang, YuXi Qian, Haoran Geng, Lianli Gao, Haiyuan Li, Bin Fang, Gao Huang, Yaodong Yang, Hao Dong, He Wang, Hang Zhao, Yadong Mu, Di Hu, Hao Zhao, Tiejun Huang, Shanghang Zhang, Yonghua Lin, Zhongyuan Wang and Guocai Yao}, journal={arXiv preprint arXiv:2511.17441}, url = {https://arxiv.org/abs/2511.17441}, year={2025}, } ``` ### Additional References If you use this dataset, please also consider citing: LeRobot Framework: https://github.com/huggingface/lerobot ## Version Information Initial Release