diff --git a/README.md b/README.md index 012ed4e98ed0483dd39e60eb5e372376a083c3fc..1fc1aa5b319328410de27258f7a0363d0300d8b3 100644 --- a/README.md +++ b/README.md @@ -1,818 +1,334 @@ --- license: cc-by-sa-4.0 -language: - - en - - zh +task_categories: +- robotics tags: - - robotics - - manipulation - - household - - bimanual-robot - - lerobot +- LeRobot +- umi +- teleoperation +- tactile +- bimanual +- garment-manipulation +size_categories: +- 1M -

PrimeBot Household Bimanual Manipulation Challenge Dataset

- - PrimeBot Household Bimanual Manipulation Challenge +# Challenge-2026 UMI 衣物操作数据集 / Challenge-2026 UMI Garment Manipulation Dataset - [中文](#中文) | [English](#english) - +English version below. ---- +## 中文说明 -# 中文 -## 目录 -- [关于我们](#关于我们) -- [更新日志](#更新日志) -- [真机遥操作数据](#真机遥操作数据) - - [训练集说明](#训练集说明) - - [验证集说明](#验证集说明) - - [数据集字段说明](#数据集字段说明) - - [URDF](#urdf) - - [图像](#图像) - - [语言指令](#语言指令) - - [本体感知与动作](#本体感知与动作) - - [机器人推理接口](#机器人推理接口) -- [UMI数据](#umi数据) - - [数据概览](#数据概览) - - [目录结构](#目录结构) - - [数据集字段说明](#数据集字段说明-1) - - [图像](#图像-1) - - [本体感知与动作](#本体感知与动作-1) - - [索引字段](#索引字段) - - [标注与 IMU](#标注与-imu) - -## 关于我们 -我们来自上纬新材-启元研究院，我们的使命是加速个人机器人时代到来，加速家用机器人时代到来。我们开源高质量面向家庭操作的双臂操作数据集，同时开放机器人硬件描述以供可视化、可复现研究。 - -> 如果本数据集对您的工作有帮助，感谢引用： - -```bibtex -@misc{xu2026scalingbimanualhouseholdmanipulation, - title={Scaling Bimanual Household Manipulation from 1,500 hours of Demonstrations to On-Policy Corrections}, - author={Jiafeng Xu and Qi Li and Yan Shen and Yiyu Ren and Travis Davies and Shaowen He and Ze Wang and Yifan Yang and Ran Cheng and Hao Dong}, - year={2026}, - eprint={2609.03591}, - archivePrefix={arXiv}, - primaryClass={cs.RO}, - url={https://arxiv.org/abs/2609.03591}, -} -``` +### 概述 -> 如果您对挑战赛、数据集或其他未尽事项有任何疑问，可通过邮件联系： -[likai@qiyuan-robot.com](mailto:likai@qiyuan-robot.com), [xujiafeng26@stu.pku.edu.cn](mailto:xujiafeng26@stu.pku.edu.cn) +本数据集是一个基于 UMI（Universal Manipulation Interface）遥操作采集流程构建的双臂衣物操作数据集，采用 LeRobot v3.0 数据规范组织。数据同步记录了多路视觉观测、末端与追踪器位姿、双爪开合以及双手触觉皮肤信号，可用于模仿学习、视觉-触觉融合的柔性物体操作策略等研究。 -# 更新日志 -- **2026.09.23**：第一批 100 小时PrimeUMI数据开源 -- **2026.09.08**：第二批 100 小时UMI数据开源 -- **2026.08.10**：第一批 5 小时UMI数据开源 -- **2026.08.06**：全部 500+ 小时真机数据开源 +数据集包含 4 个任务，均为日常衣物折叠与晾晒场景： -## 真机遥操作数据 -真机数据由启元机器人提供，数据集均以标准的 [LeRobot V2.1](https://github.com/huggingface/lerobot) 格式构造，示例Dataset代码， -``` -pip install "lerobot==0.3.3" "mmengine==0.10.7" "torch==2.7.0" "numpy==1.26.4" "torchcodec==0.5" "torchvision==0.22.0" +| 任务 ID | 任务名称 | 场景描述 | +| ------- | ---- | ------- | +| task601 | 叠短袖 | 折叠短袖上衣。 | +| task602 | 叠长袖 | 折叠长袖上衣。 | +| task613 | 晾衣服 | 将衣物晾挂。 | +| task902 | 叠T恤 | 折叠 T 恤。 | -python dataloader/custom_lerobot_dataset.py -``` +### 数据组织 + +数据按 `task{ID}_part{NNN}` 目录分片存放，每个 part 是一个独立的 LeRobot v3.0 数据包。各任务的 part 数量与规模如下： + +| 任务 | part 数 | 各 part 的 episode 数 | 合计 episode | +| ------------ | ------------------- | ----------------------------------- | ---------- | +| task601（叠短袖） | 14（part000-part013） | part000-part012 各 500，part013 为 220 | 6,720 | +| task602（叠长袖） | 2（part000-part001） | part000 为 500，part001 为 37 | 537 | +| task613（晾衣服） | 1（part000） | 19 | 19 | +| task902（叠T恤） | 1（part000） | 152 | 152 | +| 总计 | 18 | - | 约 7,428 | + +说明：以上 601 的 part001-part012 数量依据“前 13 个 part 各 500 条”的约定推算；参考样本为 task601_part000（500）与 task601_part013（220）。若个别 part 实际条数有出入，请以该 part `meta/` 下的 `delivery_manifest.json` 为准。 + +### 单个 part 目录结构 + +每个 `task{ID}_part{NNN}` 目录的根目录下包含 1 个标记文件与 3 个子文件夹： -### 训练集说明 -训练集覆盖超过12种真实的家庭场景双臂操作任务，所有数据均包含精确到帧的语言标注，部分任务列表如下 -| 任务编号 | 任务描述 | -| :--- | :--- | -| 1 | Use the gripper to fully open the washing machine door. | -| 2 | Close the washing machine door tightly with the gripper. | -| 3 | Put these two pieces of clothing into the washer. | -| 4 | Take the clothing out of the washer and put it in the basket. | -| 5 | Pick up the laundry basket with both grippers. | -| 6 | Put the dirty clothes basket on the ground. | -| 7 | Pick up the clothing and put it on the sofa. | -| 8 | Put the clothing in the folding area. | -| 9 | Unfold the clothing and fold it neatly. | -| 10 | Place the folded clothing in the storage area. | - - -训练集分4个批次，互相独立不重复，可自由组合数据集训练策略， -| 数据集名称 | 采集批次 | 任务类型 | 语言标注 | -| :--- | :--- | :--- | :--- | -| full_task_batch1_train | 1 | 1-10 | 帧级切分 | -| fold_cloth_batch2_train | 2 | 8~10 | 帧级切分 | -| partial_task_batch3_train | 3 | 1,4,5 | 帧级切分 | -| partial_task_batch4_train | 4 | 1,2,3,9 | 整段标注 | - -### 验证集说明 -验证集与训练集格式完全一致，为防止策略过拟合到state上，有以下两点特殊处理， -- 验证集中observation.state数据含有随机噪声 -- 验证集中action字段被全部置零 - -考虑到参赛团队算力资源与测试资源，本次挑战赛在不超过以下4个任务上进行线上和线下评测， -| Task ID | Task Description | -| :--- | :--- | -| 1 | Use the gripper to fully open the washing machine door. | -| 2 | Close the washing machine door tightly with the gripper. | -| 3 | Put these two pieces of clothing into the washer. | -| 9 | Unfold the clothing and fold it neatly. | - -**线上测评** -1. 参赛者需要在validation_data的全部episode上做action预测，注意数据集中action为89维，线上/线下均只需要预测25维（定义见下文的`机器人推理接口`）即可，validation_data为 - - full_task_batch1_noise_valid - - fold_cloth_batch2_noise_valid -2. 线上测评只提交预测结果的文件即可，提交入口为[Google Form](https://docs.google.com/forms/d/e/1FAIpQLSdrc5k91kazH9BLEjY17xCQ1KqAPVjmwPp5y21TT0GXQgpyKw/viewform?usp=publish-editor)。每支队伍可多次提交，多次提交需要保持队伍信息不变，我们会在测评前取最新提交结果。 -3. 提交文件的数据格式参考[YourTeamName.zip](https://huggingface.co/datasets/challenge-2026/challenge_data/tree/main/example_data/online_submission)，注意只需提交动作预测结果即可，标注、图像、本体状态等数据无需提交。 -4. 线上评估分数与动作预测误差的 MSE(Mean Squared Error) 和 MAE(Mean Absolute Error) 相关，动作预测越准确，分数越高，分数前5名入围线下测评。 -5. 测评时间在2026年8.26、8.28、8.31、9.2、9.4、9.7、9.9、9.11，在当日北京时间 10:00 AM 对提交结果统一测评，并于当天更新[Leaderboard](https://bimanual-robot-learning.github.io/challenge/)。 - -**线下测评** -1. 线下测评需要提交参赛模型的最终docker，提交形式另行通知入围队伍。 -2. 我们提供的[base docker](https://huggingface.co/datasets/challenge-2026/challenge_data/tree/main/docker)包含了推理框架和机器人运行环境，入围队伍需在此基础上构建完整docker，包含模型ckpt、运行代码、模型依赖， -3. 其他详见下文的`机器人推理接口`，有任何疑问请联系 likai@qiyuan-robot.com, yan790@pku.edu.cn. - -**Q&A (Updating)** -1. “全部episode上做action预测” 是否表示两个指定 validation 数据集中的每个episode、每一帧都需要输出 action，并保持原始 30 FPS 和 episode 长度？ - -- 两个validation数据集中，每条episode，每一帧都要输出25维action，需要保持episode长度，但我们只在选中的instruction段计算分数。 - -2. 线上评分使用什么指标？89维字段是否全部计分？ - -- 线上评估分数与动作预测误差的 MSE 和 MAE 相关。只在25维上计算分数，提交的时候也只需要提交25维action预测。 - -3. 入围真机阶段后是否允许更新模型权重？真机推理的 action chunk 长度 N、控制频率和计算资源限制分别是多少？ - -- 入围后允许更新权重。真机推理action_chunk自定，参考下文的`机器人推理接口`。机器人控制的下发频率为30Hz，线下计算资源为4090机器，显存48GB，内存64GB。 - -4. 分数计算示例 - -- 某条episode长度为1000frames，共分4段（segment内均为左闭右开），如下所示，最终提交的action预测长度为1000，维度为25，我们会在[99, 420），[420, 910)这两段上计算分数。 - - | 分段 | 片段1 | 片段2 | 片段3 | 片段4 | - | :--- | :--- | :--- | :--- | :--- | - | 索引 | 0~99(exclude) | 99~420(exclude) | 420~910(exclude) | 910~1000(exclude) | - | 标注 | Start remote operation. | Open the washing machine door. | Close the washing machine door. | End remote operation. | - -### 数据集字段说明 -#### URDF -我们开放机器人硬件描述以支持非商业用途的教学、实验、研究等项目。可从以下地址获取URDF：`challenge_data/robot_description/x2w.urdf`。 - -#### 图像 -包含三视角RGB图像，分辨率为1280*720，帧率30FPS，字段定义如下 -| 数据集字段 | 数据源 | -| :--- | :--- | -| observation.images.x2w_camera_head_realsense_compressed | 头部相机 | -| observation.images.x2w_camera_left_wrist_zedxonegs_rgb_raw_image_compressed | 左手相机 | -| observation.images.x2w_camera_right_wrist_zedxonegs_rgb_raw_image_compressed | 右手相机 | - -#### 语言指令 -数据集全部标注信息见${dataset_name}/meta/info.json。每段语言标注均为左闭右开，即[start_frame_index, end_frame_index），以一条1000帧的操作任务为例说明如下 -| 分段 | 片段1 | 片段2 | 片段3 | 片段4 | -| :--- | :--- | :--- | :--- | :--- | -| 索引 | 0~99(exclude) | 99~420 | 420~910 | 910~1000 | -| 标注 | Start remote operation. | Open the washing machine door. | Close the washing machine door. | End remote operation. | - -#### 本体感知与动作 -包括机器人状态信息（observation.state）与动作信息（action），维度均为89维，定义如下 -##### 1. 关节位置（Joint Position，索引 0-21） -| 索引 | 数据源 | 列名 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | :--- | -| 0 | joint_state | folding_lower_joint | 折叠下关节角度 | rad | -| 1 | joint_state | folding_upper_joint | 折叠上关节角度 | rad | -| 2 | joint_state | waist_pitch_joint | 腰部俯仰关节角度 | rad | -| 3 | joint_state | torso_yaw_joint | 躯干偏航关节角度 | rad | -| 4 | joint_state | head_yaw_joint | 头部偏航关节角度 | rad | -| 5 | joint_state | head_pitch_joint | 头部俯仰关节角度 | rad | -| 6 | joint_state | left_shoulder_pitch_joint | 左肩俯仰关节角度 | rad | -| 7 | joint_state | left_shoulder_roll_joint | 左肩翻滚关节角度 | rad | -| 8 | joint_state | left_shoulder_yaw_joint | 左肩偏航关节角度 | rad | -| 9 | joint_state | left_elbow_pitch_joint | 左肘俯仰关节角度 | rad | -| 10 | joint_state | left_wrist_roll_joint | 左腕翻滚关节角度 | rad | -| 11 | joint_state | left_wrist_yaw_joint | 左腕偏航关节角度 | rad | -| 12 | joint_state | left_wrist_pitch_joint | 左腕俯仰关节角度 | rad | -| 13 | joint_state | right_shoulder_pitch_joint | 右肩俯仰关节角度 | rad | -| 14 | joint_state | right_shoulder_roll_joint | 右肩翻滚关节角度 | rad | -| 15 | joint_state | right_shoulder_yaw_joint | 右肩偏航关节角度 | rad | -| 16 | joint_state | right_elbow_pitch_joint | 右肘俯仰关节角度 | rad | -| 17 | joint_state | right_wrist_roll_joint | 右腕翻滚关节角度 | rad | -| 18 | joint_state | right_wrist_yaw_joint | 右腕偏航关节角度 | rad | -| 19 | joint_state | right_wrist_pitch_joint | 右腕俯仰关节角度 | rad | -| 20 | joint_state | left_finger_l_joint | 左手指关节角度 | rad | -| 21 | joint_state | right_finger_l_joint | 右手指关节角度 | rad | - -##### 2. 关节速度（Joint Velocity，索引 22-43） -| 索引 | 数据源 | 列名 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | :--- | -| 22 | joint_state | folding_lower_joint_velocity | 折叠下关节角速度 | rad/s | -| 23 | joint_state | folding_upper_joint_velocity | 折叠上关节角速度 | rad/s | -| 24 | joint_state | waist_pitch_joint_velocity | 腰部俯仰关节角速度 | rad/s | -| 25 | joint_state | torso_yaw_joint_velocity | 躯干偏航关节角速度 | rad/s | -| 26 | joint_state | head_yaw_joint_velocity | 头部偏航关节角速度 | rad/s | -| 27 | joint_state | head_pitch_joint_velocity | 头部俯仰关节角速度 | rad/s | -| 28 | joint_state | left_shoulder_pitch_joint_velocity | 左肩俯仰关节角速度 | rad/s | -| 29 | joint_state | left_shoulder_roll_joint_velocity | 左肩翻滚关节角速度 | rad/s | -| 30 | joint_state | left_shoulder_yaw_joint_velocity | 左肩偏航关节角速度 | rad/s | -| 31 | joint_state | left_elbow_pitch_joint_velocity | 左肘俯仰关节角速度 | rad/s | -| 32 | joint_state | left_wrist_roll_joint_velocity | 左腕翻滚关节角速度 | rad/s | -| 33 | joint_state | left_wrist_yaw_joint_velocity | 左腕偏航关节角速度 | rad/s | -| 34 | joint_state | left_wrist_pitch_joint_velocity | 左腕俯仰关节角速度 | rad/s | -| 35 | joint_state | right_shoulder_pitch_joint_velocity | 右肩俯仰关节角速度 | rad/s | -| 36 | joint_state | right_shoulder_roll_joint_velocity | 右肩翻滚关节角速度 | rad/s | -| 37 | joint_state | right_shoulder_yaw_joint_velocity | 右肩偏航关节角速度 | rad/s | -| 38 | joint_state | right_elbow_pitch_joint_velocity | 右肘俯仰关节角速度 | rad/s | -| 39 | joint_state | right_wrist_roll_joint_velocity | 右腕翻滚关节角速度 | rad/s | -| 40 | joint_state | right_wrist_yaw_joint_velocity | 右腕偏航关节角速度 | rad/s | -| 41 | joint_state | right_wrist_pitch_joint_velocity | 右腕俯仰关节角速度 | rad/s | -| 42 | joint_state | left_finger_l_joint_velocity | 左手指关节角速度 | rad/s | -| 43 | joint_state | right_finger_l_joint_velocity | 右手指关节角速度 | rad/s | - -##### 3. 关节力矩（Joint Effort，索引 44-65） -| 索引 | 数据源 | 列名 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | :--- | -| 44 | joint_state | folding_lower_joint_effort | 折叠下关节输出力矩 | N·m | -| 45 | joint_state | folding_upper_joint_effort | 折叠上关节输出力矩 | N·m | -| 46 | joint_state | waist_pitch_joint_effort | 腰部俯仰关节输出力矩 | N·m | -| 47 | joint_state | torso_yaw_joint_effort | 躯干偏航关节输出力矩 | N·m | -| 48 | joint_state | head_yaw_joint_effort | 头部偏航关节输出力矩 | N·m | -| 49 | joint_state | head_pitch_joint_effort | 头部俯仰关节输出力矩 | N·m | -| 50 | joint_state | left_shoulder_pitch_joint_effort | 左肩俯仰关节输出力矩 | N·m | -| 51 | joint_state | left_shoulder_roll_joint_effort | 左肩翻滚关节输出力矩 | N·m | -| 52 | joint_state | left_shoulder_yaw_joint_effort | 左肩偏航关节输出力矩 | N·m | -| 53 | joint_state | left_elbow_pitch_joint_effort | 左肘俯仰关节输出力矩 | N·m | -| 54 | joint_state | left_wrist_roll_joint_effort | 左腕翻滚关节输出力矩 | N·m | -| 55 | joint_state | left_wrist_yaw_joint_effort | 左腕偏航关节输出力矩 | N·m | -| 56 | joint_state | left_wrist_pitch_joint_effort | 左腕俯仰关节输出力矩 | N·m | -| 57 | joint_state | right_shoulder_pitch_joint_effort | 右肩俯仰关节输出力矩 | N·m | -| 58 | joint_state | right_shoulder_roll_joint_effort | 右肩翻滚关节输出力矩 | N·m | -| 59 | joint_state | right_shoulder_yaw_joint_effort | 右肩偏航关节输出力矩 | N·m | -| 60 | joint_state | right_elbow_pitch_joint_effort | 右肘俯仰关节输出力矩 | N·m | -| 61 | joint_state | right_wrist_roll_joint_effort | 右腕翻滚关节输出力矩 | N·m | -| 62 | joint_state | right_wrist_yaw_joint_effort | 右腕偏航关节输出力矩 | N·m | -| 63 | joint_state | right_wrist_pitch_joint_effort | 右腕俯仰关节输出力矩 | N·m | -| 64 | joint_state | left_finger_l_joint_effort | 左手指关节输出力矩 | N·m | -| 65 | joint_state | right_finger_l_joint_effort | 右手指关节输出力矩 | N·m | - -##### 4. 末端执行器位姿（Gripper Pose，索引 66-79） -| 索引 | 数据源 | 列名 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | :--- | -| 66 | gripper_pose | left_gripper_x | 左末端执行器 X 位置 | m | -| 67 | gripper_pose | left_gripper_y | 左末端执行器 Y 位置 | m | -| 68 | gripper_pose | left_gripper_z | 左末端执行器 Z 位置 | m | -| 69 | gripper_pose | left_gripper_qx | 左末端执行器四元数 X 分量 | - | -| 70 | gripper_pose | left_gripper_qy | 左末端执行器四元数 Y 分量 | - | -| 71 | gripper_pose | left_gripper_qz | 左末端执行器四元数 Z 分量 | - | -| 72 | gripper_pose | left_gripper_qw | 左末端执行器四元数 W 分量 | - | -| 73 | gripper_pose | right_gripper_x | 右末端执行器 X 位置 | m | -| 74 | gripper_pose | right_gripper_y | 右末端执行器 Y 位置 | m | -| 75 | gripper_pose | right_gripper_z | 右末端执行器 Z 位置 | m | -| 76 | gripper_pose | right_gripper_qx | 右末端执行器四元数 X 分量 | - | -| 77 | gripper_pose | right_gripper_qy | 右末端执行器四元数 Y 分量 | - | -| 78 | gripper_pose | right_gripper_qz | 右末端执行器四元数 Z 分量 | - | -| 79 | gripper_pose | right_gripper_qw | 右末端执行器四元数 W 分量 | - | - -##### 5. 轮子关节状态（Wheel Joint State，索引 80-88） -| 索引 | 数据源 | 列名 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | :--- | -| 80 | wheel_joint_state | wheel_front_left_position | 前左轮角度位置 | rad | -| 81 | wheel_joint_state | wheel_front_right_position | 前右轮角度位置 | rad | -| 82 | wheel_joint_state | wheel_rear_position | 后轮角度位置 | rad | -| 83 | wheel_joint_state | wheel_front_left_velocity | 前左轮角速度 | rad/s | -| 84 | wheel_joint_state | wheel_front_right_velocity | 前右轮角速度 | rad/s | -| 85 | wheel_joint_state | wheel_rear_velocity | 后轮角速度 | rad/s | -| 86 | wheel_joint_state | wheel_front_left_effort | 前左轮输出力矩 | N·m | -| 87 | wheel_joint_state | wheel_front_right_effort | 前右轮输出力矩 | N·m | -| 88 | wheel_joint_state | wheel_rear_effort | 后轮输出力矩 | N·m | - -### 机器人推理接口 -我们提供了包含机器人运行环境的基础Docker镜像，参赛者需要实现自己模型的两个函数 ``` -def load_model(self): - # TODO: implement model loading - pass - -def predict(self, sample: dict) -> np.ndarray: - # TODO: implement inference, return np.ndarray of shape (N, 25) - pass - -def inference_loop(self): - last_iner_time = time.time() - while self.running: - if self.prepare_in_progress or not self.enable_inference: - time.sleep(0.1) - continue - - tic = time.time() - sample = self.update_input() - prediction = self.predict(sample) +task{ID}_part{NNN}/ +├── _SUCCESS # 打包成功标记（位于 part 根目录） +├── data/ # 帧级状态与动作数据（parquet 分片） +│ └── chunk-{NNN}/ +│ └── file-{NNN}.parquet +├── meta/ # 元信息文件 +│ ├── info.json +│ ├── tasks.parquet +│ ├── stats.json +│ ├── delivery_manifest.json +│ └── episodes/ +│ └── chunk-000/ +│ └── file-000.parquet # episode 级元数据 +└── videos/ # 三路视频，各成一个子文件夹 + ├── observation.images.pico/ + │ └── chunk-{NNN}/file-{NNN}.mp4 + ├── observation.images.left/ + │ └── chunk-{NNN}/file-{NNN}.mp4 + └── observation.images.right/ + └── chunk-{NNN}/file-{NNN}.mp4 +``` + +各组成部分说明： + +| 部分 | 位置 | 说明 | +| -------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------- | +| _SUCCESS | part 根目录 | 打包成功标记文件，表示该 part 交付完整。 | +| data/ | part 根目录 | 帧级数据，按 LeRobot 约定分块存放于 `chunk-{NNN}/file-{NNN}.parquet`，含 state、action、timestamp 及索引字段。 | +| meta/ | part 根目录 | 元信息文件夹，包含 info.json、tasks.parquet、stats.json、delivery_manifest.json 及 episode 级元数据 parquet。 | +| videos/ | part 根目录 | 视频文件夹，含 `observation.images.pico/`、`observation.images.left/`、`observation.images.right/` 三个子文件夹，各自按 `chunk-{NNN}/file-{NNN}.mp4` 分块。 | + +`meta/` 内文件明细： + +| 文件 | 说明 | +| ----------------------------------- | ----------------------------------------------------------------------- | +| info.json | 数据包元信息：特征定义、帧率、划分、视频参数等。 | +| tasks.parquet | 任务索引表，task_index 到该包的 task_id。 | +| stats.json | 各特征（state / action 等）的统计量：min、max、mean、std、count、q01、q99。 | +| delivery_manifest.json | 交付清单：task_id、package_index、episode_count、duration_s 及全部源 episode ID 列表。 | +| episodes/chunk-000/file-000.parquet | Episode 级元数据表（每行一个 episode），记录时间戳、源 bag、机器人 ID、数据/视频分片索引等。 | + +数据与视频遵循 LeRobot 分片命名约定： + ``` -数据集中虽然记录了完整的action字段，但在推理中我们只选择机器人关节位置（25维）作为控制指令，完整字段定义如下 - -| 索引 | 列名 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | -| 0 | folding_lower_joint | 折叠下关节角度 | rad | -| 1 | folding_upper_joint | 折叠上关节角度 | rad | -| 2 | waist_pitch_joint | 腰部俯仰关节角度 | rad | -| 3 | torso_yaw_joint | 躯干偏航关节角度 | rad | -| 4 | head_yaw_joint | 头部偏航关节角度 | rad | -| 5 | head_pitch_joint | 头部俯仰关节角度 | rad | -| 6 | left_shoulder_pitch_joint | 左肩俯仰关节角度 | rad | -| 7 | left_shoulder_roll_joint | 左肩翻滚关节角度 | rad | -| 8 | left_shoulder_yaw_joint | 左肩偏航关节角度 | rad | -| 9 | left_elbow_pitch_joint | 左肘俯仰关节角度 | rad | -| 10 | left_wrist_roll_joint | 左腕翻滚关节角度 | rad | -| 11 | left_wrist_yaw_joint | 左腕偏航关节角度 | rad | -| 12 | left_wrist_pitch_joint | 左腕俯仰关节角度 | rad | -| 13 | right_shoulder_pitch_joint | 右肩俯仰关节角度 | rad | -| 14 | right_shoulder_roll_joint | 右肩翻滚关节角度 | rad | -| 15 | right_shoulder_yaw_joint | 右肩偏航关节角度 | rad | -| 16 | right_elbow_pitch_joint | 右肘俯仰关节角度 | rad | -| 17 | right_wrist_roll_joint | 右腕翻滚关节角度 | rad | -| 18 | right_wrist_yaw_joint | 右腕偏航关节角度 | rad | -| 19 | right_wrist_pitch_joint | 右腕俯仰关节角度 | rad | -| 20 | left_finger_l_joint | 左手指关节角度 | rad | -| 21 | right_finger_l_joint | 右手指关节角度 | rad | -| 22 | wheel_front_left_velocity | 前左轮角速度 | rad/s | -| 23 | wheel_front_right_velocity | 前右轮角速度 | rad/s | -| 24 | wheel_rear_velocity | 后轮角速度 | rad/s | - - -## UMI数据 -UMI数据由[上海朗智格机器人科技有限公司](http://crobotia.com/)与启元机器人共同提供，构造为标准的 **LeRobot v3.0** 数据，数据描述详见各批次的README。 - -### 数据概览 - -| Episode | `task_index` | 任务 | 帧数 | 时长 | -| :--- | ---: | :--- | ---: | ---: | -| `episode_000000` | 0 | fold the red shirt | 1410 | 47 s | -| `episode_000001` | 1 | fold the black shirt | 1050 | 35 s | -| `episode_000002` | 2 | fold the yellow shirt | 870 | 29 s | -| `episode_000003` | 1 | fold the black shirt | 1050 | 35 s | -| `episode_000004` | 3 | fold the brown shirt | 1560 | 52 s | - -数据集共 5940 帧、4 个唯一任务，帧率为 30 FPS，ego 视频分辨率为 960 × 960。 - -### 目录结构 - -```text -umi_sample_data_v21/ -├── data/chunk-000/ # 5 个 episode Parquet 文件 -├── videos/chunk-000/ -│ ├── observation.images.left_ego/ # 左手 ego 视频 -│ └── observation.images.right_ego/ # 右手 ego 视频 -├── meta/ -│ ├── info.json # 数据集与字段定义 -│ ├── tasks.jsonl # 任务与 task_index 映射 -│ ├── episodes.jsonl # episode 长度与任务 -│ ├── episodes_stats.jsonl # 每个 episode 的统计量 -│ └── calibration.json # 相机与 IMU 标定参数 -├── annotation/ # episode 级任务和动作分段标注 -└── imu/ # 左右手 IMU 数据 +data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet +videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4 ``` -### 数据集字段说明 +### 采集与标注方式 -#### 图像 +数据通过 UMI 遥操作装置采集，采集与标注流程如下： -| 数据集字段 | 数据源 | -| :--- | :--- | -| `observation.images.left_ego` | 左手腕载 RGB ego 相机，960 × 960 | -| `observation.images.right_ego` | 右手腕载 RGB ego 相机，960 × 960 | +- 遥操作采集：操作者手持 UMI 夹爪，佩戴带 Pico 头显与 Tracker 追踪器的装置完成双臂衣物操作；每次连续操作记录为一个 episode（源命名形如 `episode_YYYYMMDD_hhmmss_YYYYMMDD_hhmmss`，包含起止时间戳）。 +- 视觉记录：pico 相机与左右手腕相机以 30 fps 同步录制 h264 视频，分别写入 `videos/` 下三个子文件夹。 +- 位姿标注：主相机（Rigid_Pico）与左右锚点物体（Rigid_Tracker1/2）的 6-DoF 位姿由光学/惯性追踪系统标定并记录为“位置 xyz + 四元数 xyzw”，写入 `data/` 的 observation.state。 +- 夹爪与触觉标注：夹爪开合距离（real_distance）由夹爪传感器读取；双手触觉皮肤压力矩阵（skin_sensor_left/right）逐帧记录并展平为向量。 +- 时间对齐：所有模态按统一时间戳对齐到 30 fps 帧序列，`timestamp`、`frame_index`、`episode_index`、`index` 保证帧与视频、episode 可索引。 +- 任务标注：每个数据包对应一个 task_id（601/602/613/902），并通过 `meta/tasks.parquet` 建立 task_index 到任务的映射；上表给出了各 task_id 对应的语义标签。 +- 动作标签：`action` 取自左右夹爪的目标开合距离（real_distance），可作为模仿学习的监督信号。 +- 质检交付：每个 part 打包完成后在根目录写入 `_SUCCESS` 标记，并在 `meta/delivery_manifest.json` 记录 episode 数、时长与源 episode 列表，用于交付完整性校验。 -#### 本体感知与动作 +### 视觉观测 Video -`observation.state` 和 `action` 均为 16 维，字段顺序一致： +| 视频键（对应 videos/ 子文件夹） | 分辨率 H x W x C | 编码 | 帧率 | +| ------------------------ | --------------- | -------------- | --- | +| observation.images.pico | 768 x 1024 x 3 | h264 / yuv420p | 30 | +| observation.images.left | 1536 x 1920 x 3 | h264 / yuv420p | 30 | +| observation.images.right | 1536 x 1920 x 3 | h264 / yuv420p | 30 | -| 索引 | 字段顺序 | 物理意义 | 单位 | -| :--- | :--- | :--- | :--- | -| 0–2 | `left_x, left_y, left_z` | 左手末端位置 | m | -| 3–6 | `left_qw, left_qx, left_qy, left_qz` | 左手末端四元数 `(w, x, y, z)` | - | -| 7 | `left_gripper` | 左夹爪开合角度 | ° | -| 8–10 | `right_x, right_y, right_z` | 右手末端位置 | m | -| 11–14 | `right_qw, right_qx, right_qy, right_qz` | 右手末端四元数 `(w, x, y, z)` | - | -| 15 | `right_gripper` | 右夹爪开合角度 | ° | +所有视频均无音轨、非深度图。 -`observation.state` 表示当前帧状态。除末帧外，`action[t] = state[t+1]`；末帧 action 保留原始采集序列的下一时刻目标，因此不一定等于本 episode 的末帧 state。左右手位姿使用独立坐标系，不能直接计算双手之间的相对距离或姿态。 +### 状态观测 observation.state（791 维，float32） -#### 索引字段 +| 子字段 | 维度 | 索引区间 | 含义 | +| ------------------------ | --- | ------- | --------------------------------------------- | +| main_camera_pose | 7 | 0-7 | 主相机（Rigid_Pico）的世界位姿：位置 xyz + 四元数 xyzw。 | +| anchor_object_left_pose | 7 | 7-14 | 左臂锚点物体（AnchorObject_L / Rigid_Tracker1）的世界位姿。 | +| anchor_object_right_pose | 7 | 14-21 | 右臂锚点物体（AnchorObject_R / Rigid_Tracker2）的世界位姿。 | +| gripper_distance | 2 | 21-23 | 左、右夹爪的实测开合距离 real_distance。 | +| left_hand_skin_left | 192 | 23-215 | 左手 skin_sensor_left.csv 触觉矩阵展平向量。 | +| left_hand_skin_right | 192 | 215-407 | 左手 skin_sensor_right.csv 触觉矩阵展平向量。 | +| right_hand_skin_left | 192 | 407-599 | 右手 skin_sensor_left.csv 触觉矩阵展平向量。 | +| right_hand_skin_right | 192 | 599-791 | 右手 skin_sensor_right.csv 触觉矩阵展平向量。 | -| 字段 | 类型 | 说明 | -| :--- | :--- | :--- | -| `timestamp` | float32 | episode 内时间，单位为秒 | -| `frame_index` | int64 | episode 内帧编号，从 0 开始 | -| `episode_index` | int64 | episode 编号，范围为 0–4 | -| `index` | int64 | 数据集全局帧编号，范围为 0–5939 | -| `task_index` | int64 | 任务编号，对应 `meta/tasks.jsonl` | +位姿均为“位置 xyz + 四元数 xyzw”的 7 维表示；触觉皮肤为二维压力矩阵展平后的 192 维向量。 -#### 标注与 IMU +### 动作 action（2 维，float32） -| 文件 | 说明 | -| :--- | :--- | -| `annotation/episode_subtasks_*.jsonl` | episode 级任务、目标物体与成功状态 | -| `annotation/action_steps_*.jsonl` | 细粒度动作分段 | -| `imu/episode_*_{left,right}.csv` | 左右手时间戳、三轴角速度和三轴加速度 | +| 子字段 | 维度 | 索引区间 | 含义 | +| ---------------- | --- | ---- | --------------------------------------- | +| gripper_distance | 2 | 0-2 | 左、右夹爪的目标开合距离 real_distance。取值范围约 0-118。 | -标注区间采用 `[start_frame_index, end_frame_index)`，即包含起始帧、不包含结束帧。 +### 其他帧字段 ---- +timestamp（float32）、frame_index（int64）、episode_index（int64）、index（int64）、task_index（int64）。 -# English -## Contents -- [About Us](#about-us) -- [Change Log](#change-log) -- [Real-World Teleoperation Data](#real-world-teleoperation-data) - - [Training Set Description](#training-set-description) - - [Validation Set Description](#validation-set-description) - - [Dataset Field Description](#dataset-field-description) - - [URDF](#urdf) - - [Camera Images](#camera-images) - - [Language Instructions](#language-instructions) - - [Proprioceptive and Actions](#proprioceptive-and-actions) - - [Robot Inference Interface](#robot-inference-interface) -- [UMI Data](#umi-data) - - [Dataset Overview](#dataset-overview) - - [Directory Structure](#directory-structure) - - [Dataset Fields](#dataset-fields) - - [Camera Images](#camera-images-1) - - [Proprioception and Actions](#proprioception-and-actions) - - [Index Fields](#index-fields) - - [Annotations and IMU](#annotations-and-imu) - -## About Us -We come from PrimeBot Research Institute, Swancor Advanced Materials Co., Ltd. Our mission is to accelerate the advent of the era of personal robots and home robots. We open-source a high-quality, home-oriented bimanual manipulation dataset, and also provide open-source robot hardware descriptions for visualization and reproducible research. - -> If this dataset is helpful to your work, please cite it: - -```bibtex -@misc{xu2026scalingbimanualhouseholdmanipulation, - title={Scaling Bimanual Household Manipulation from 1,500 hours of Demonstrations to On-Policy Corrections}, - author={Jiafeng Xu and Qi Li and Yan Shen and Yiyu Ren and Travis Davies and Shaowen He and Ze Wang and Yifan Yang and Ran Cheng and Hao Dong}, - year={2026}, - eprint={2609.03591}, - archivePrefix={arXiv}, - primaryClass={cs.RO}, - url={https://arxiv.org/abs/2609.03591}, -} -``` +### 公共规格（各 part 一致） -> If you have any questions about the challenge, dataset, or other information, feel free to drop an email: -[likai@qiyuan-robot.com](mailto:likai@qiyuan-robot.com), [xujiafeng26@stu.pku.edu.cn](mailto:xujiafeng26@stu.pku.edu.cn) +| 项目 | 数值 | +| ---------------------- | ---------------------- | +| 代码库版本 codebase_version | v3.0 | +| 机器人类型 robot_type | umi | +| 采样帧率 fps | 30 | +| 状态维度 | 791 | +| 动作维度 | 2 | +| 视频路数 | 3（pico / left / right） | -## Change Log -- **2026.09.23**: First batch of 100 hours of PrimeUMI data released open source -- **2026.09.08**: Second batch of 100 hours of UMI data released open source -- **2026.08.10**: First batch of 5 hours of UMI data released open source -- **2026.08.06**: All 500+ hours of real robot data released open source +### 加载方式 -## Real-World Teleoperation Data -Teleoperation data is sponsored by PrimeBot. The dataset is constructed in the standard [LeRobot V2.1](https://github.com/huggingface/lerobot) format. Example code for loading the dataset: -```bash -pip install "lerobot==0.3.3" "mmengine==0.10.7" "torch==2.7.0" "numpy==1.26.4" "torchcodec==0.5" "torchvision==0.22.0" +每个 part 是一个完整的 LeRobot 数据集（含 data/、meta/、videos/），可直接按 part 根目录加载；如需合并训练，可按任务拼接多个 part： -python dataloader/custom_lerobot_dataset.py +```python +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + +dataset = LeRobotDataset("your-org/challenge_data", root="task601_part000") +print(dataset) +sample = dataset[0] ``` -### Training Set Description -The training set covers more than 12 real-world dual-arm manipulation tasks in household scenarios. All data includes frame-accurate language annotations. Partial task list: +### 许可 + +如无特殊说明，本数据集以 cc-by-sa-4.0 许可发布。使用前请确认符合各源数据的授权要求。 + +--- + +## English + +### Overview -| Task ID | Task Description | -| :--- | :--- | -| 1 | Use the gripper to fully open the washing machine door. | -| 2 | Close the washing machine door tightly with the gripper. | -| 3 | Put these two pieces of clothing into the washer. | -| 4 | Take the clothing out of the washer and put it in the basket. | -| 5 | Pick up the laundry basket with both grippers. | -| 6 | Put the dirty clothes basket on the ground. | -| 7 | Pick up the clothing and put it on the sofa. | -| 8 | Put the clothing in the folding area. | -| 9 | Unfold the clothing and fold it neatly. | -| 10 | Place the folded clothing in the storage area. | +This dataset is a bimanual garment manipulation dataset collected through a UMI (Universal Manipulation Interface) teleoperation pipeline and organized under the LeRobot v3.0 format. It jointly records multi-view visual observations, end-effector and tracker poses, gripper openings, and dual-hand tactile skin signals, supporting research on imitation learning and vision-tactile deformable-object manipulation policies. -The training set consists of four independent, non-overlapping subsets. You can freely combine them for different training strategies. -| Dataset Name | Collection Batch | Task Type | Instruction | -| :--- | :--- | :--- | :--- | -| full_task_batch1_train | 1 | 1-10 | frame-level | -| fold_cloth_batch2_train | 2 | 8~10 | frame-level | -| partial_task_batch3_train | 3 | 1,4,5 | frame-level | -| partial_task_batch4_train | 4 | 1,2,3,9 | segments-level | +The dataset contains 4 tasks, all in everyday garment folding and hanging scenarios: + +| Task ID | Task name | Scene | +| ------- | ----------------------- | --------------------------- | +| task601 | Fold short-sleeve shirt | Folding a short-sleeve top. | +| task602 | Fold long-sleeve shirt | Folding a long-sleeve top. | +| task613 | Hang clothes | Hanging garments to dry. | +| task902 | Fold T-shirt | Folding a T-shirt. | + +### Data Organization + +Data is sharded into `task{ID}_part{NNN}` directories; each part is a self-contained LeRobot v3.0 package. Part counts and sizes per task: + +| Task | Parts | Episodes per part | Total episodes | +| --------------------------- | -------------------- | --------------------------------------------- | -------------- | +| task601 (fold short-sleeve) | 14 (part000-part013) | 500 each for part000-part012, 220 for part013 | 6,720 | +| task602 (fold long-sleeve) | 2 (part000-part001) | 500 for part000, 37 for part001 | 537 | +| task613 (hang clothes) | 1 (part000) | 19 | 19 | +| task902 (fold T-shirt) | 1 (part000) | 152 | 152 | +| Total | 18 | - | ~7,428 | + +Note: the counts for task601 part001-part012 are inferred from the "500 episodes each for the first 13 parts" convention; reference samples are task601_part000 (500) and task601_part013 (220). If any part differs, treat that part's `meta/delivery_manifest.json` as authoritative. + +### Per-Part Directory Structure + +Each `task{ID}_part{NNN}` directory contains one marker file and three subfolders at its root: -During the online assessment, participants must submit all predicted action trajectories on the validation_data dataset (scores are only calculated for the tasks mentioned above). The assessment dataset is: -- full_task_batch1_noise_valid -- fold_cloth_batch2_noise_valid - -### Validation Set Description -The validation set follows exactly the same format as the training set. To prevent policies from overfitting to state data, two special adjustments are applied: -- The `observation.state` data in the validation set contains random noise. -- All `action` fields in the validation set are set to zero. - -Considering the computing resources of participating teams, the evaluation of this challenge will be conducted on no more than the follow 4 tasks: -| Task ID | Task Description | -| :--- | :--- | -| 1 | Use the gripper to fully open the washing machine door. | -| 2 | Close the washing machine door tightly with the gripper. | -| 3 | Put these two pieces of clothing into the washer. | -| 9 | Unfold the clothing and fold it neatly. | - -**Online evaluation** -1. Participants need to predict actions across all episodes of the validation_data. Note that the dataset contains 89 dimensions for actions; however, only 25 dimensions need to be predicted for both online and on-site evaluation participation (see `Robot Inference Interface` for definition). The validation_data consists of: - - full_task_batch1_noise_valid - - fold_cloth_batch2_noise_valid - -2. For online evaluation, only the file containing the prediction results needs to be submitted. The submission portal is [Google Form](https://docs.google.com/forms/d/e/1FAIpQLSdrc5k91kazH9BLEjY17xCQ1KqAPVjmwPp5y21TT0GXQgpyKw/viewform?usp=publish-editor). Each team can submit multiple times, ensuring that the team information remains unchanged. We will use the latest submission before the evaluation. - -3. The data format for submitted files should refer to [YourTeamName.zip](https://huggingface.co/datasets/challenge-2026/challenge_data/tree/main/example_data/online_submission). Note that only action prediction results are required; annotations, images, ontology state, etc., are not needed. - -4. Online evaluation scores are related to the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of action prediction errors. The more accurate the action prediction, the higher the score. The top 5 teams will advance to the on-site evaluation. - -5. The evaluation will be conducted on August 26, 28, 31, September 2, 4, 7, 9, and 11, 2026. The submitted results will be evaluated uniformly at 10:00 AM Beijing time on the same day, and the [Leaderboard](https://bimanual-robot-learning.github.io/challenge/) will be updated on the same day. - -**On-site evaluation** -1. For the on-site evaluation, teams need to submit the final Docker image of their participating model. The submission method will be notified to the shortlisted teams separately. - -2. The [base docker](https://huggingface.co/datasets/challenge-2026/challenge_data/tree/main/docker) we provide includes the inference framework and the robot's runtime environment. Shortlisted teams must build a complete Docker container on top of this, including the model (ckpt), runtime code, and model dependencies. - -3. For further details, please see `Robot Inference Interface`. For any questions, please contact likai@qiyuan-robot.com, yan790@pku.edu.cn. - -**Q&A (Updating)** -1. Does "Perform action prediction on all episodes" mean that every episode and every frame in the two specified validation datasets needs to output an action while maintaining the original 30 FPS and episode length? - -- For each episode and every frame in the two validation datasets, a 25-dimensional action needs to be output, maintaining the episode length, but we only calculate scores for the selected instruction segments. - -2. What metrics are used for online scoring? Are all 89 dimensions scored? - -- The online evaluation score is related to the MSE and MAE of the action prediction error. Scores are only calculated on the 25 dimensions, and only the 25-dimensional action prediction needs to be submitted. - -3. Is it allowed to update model weights after being shortlisted for the real machine stage? What are the action chunk length N, control frequency, and computational resource limits for real machine inference? - -- Weight updates are allowed after being shortlisted. The action_chunk for real machine inference is customizable; refer to `Robot Inference Interface`. The robot control sends commands at a frequency of 30Hz, and the on-site computing resources consist of a 4090 machine with 48GB of VRAM and 64GB of RAM. - -4. Score Calculation Example - -- An episode is 1000 frames long, divided into 4 segments (each segment is left-closed and right-open), as shown below. The final submitted action prediction is 1000 frames long with a dimension of 25. We will calculate the score on the segments [99, 420) and [420, 910). - - | Segmentation | Segment 1 | Segment 2 | Segment 3 | Segment 4 | - | :--- | :--- | :--- | :--- | :--- | - | Index | 0~99(exclude) | 99~420(exclude) | 420~910(exclude) | 910~1000(exclude) | - | Annotation | Start remote operation. | Open the washing machine door. | Close the washing machine door. | End remote operation. | - - -### Dataset Field Description -#### URDF -We are releasing the robot hardware description to support non-commercial projects such as teaching, experimentation, and research. The URDF can be obtained from the following address: `challenge_data/robot_description`. - -#### Camera Images -The dataset includes three-view RGB images with a resolution of 1280×720 at 30 FPS. Field definitions: - -| Dataset Field | Source | -| :--- | :--- | -| observation.images.x2w_camera_head_realsense_compressed | Head camera | -| observation.images.x2w_camera_left_wrist_zedxonegs_rgb_raw_image_compressed | Left wrist camera | -| observation.images.x2w_camera_right_wrist_zedxonegs_rgb_raw_image_compressed | Right wrist camera | - -#### Language Instructions -The complete annotation information for the dataset can be found in `${dataset_name}/meta/info.json`. Each language annotation is left-closed and right-open, i.e., [start_frame_index, end_frame_index). Taking a 1000-frame manipulation task as an example: - -| Segment | Segment 1 | Segment 2 | Segment 3 | Segment 4 | -| :--- | :--- | :--- | :--- | :--- | -| Frame Index | 0–99(exclude) | 99–420 | 420–910 | 910–1000 | -| Annotation | Start remote operation. | Open the washing machine door. | Close the washing machine door. | End remote operation. | - -#### Proprioceptive and Actions -Including robot state (`observation.state`) and action (`action`), both with 89 dimensions, defined as follows: - -##### 1. Joint Position (Index 0–21) -| Index | Source | Column Name | Physical Meaning | Unit | -| :--- | :--- | :--- | :--- | :--- | -| 0 | joint_state | folding_lower_joint | Folding lower joint angle | rad | -| 1 | joint_state | folding_upper_joint | Folding upper joint angle | rad | -| 2 | joint_state | waist_pitch_joint | Waist pitch joint angle | rad | -| 3 | joint_state | torso_yaw_joint | Torso yaw joint angle | rad | -| 4 | joint_state | head_yaw_joint | Head yaw joint angle | rad | -| 5 | joint_state | head_pitch_joint | Head pitch joint angle | rad | -| 6 | joint_state | left_shoulder_pitch_joint | Left shoulder pitch joint angle | rad | -| 7 | joint_state | left_shoulder_roll_joint | Left shoulder roll joint angle | rad | -| 8 | joint_state | left_shoulder_yaw_joint | Left shoulder yaw joint angle | rad | -| 9 | joint_state | left_elbow_pitch_joint | Left elbow pitch joint angle | rad | -| 10 | joint_state | left_wrist_roll_joint | Left wrist roll joint angle | rad | -| 11 | joint_state | left_wrist_yaw_joint | Left wrist yaw joint angle | rad | -| 12 | joint_state | left_wrist_pitch_joint | Left wrist pitch joint angle | rad | -| 13 | joint_state | right_shoulder_pitch_joint | Right shoulder pitch joint angle | rad | -| 14 | joint_state | right_shoulder_roll_joint | Right shoulder roll joint angle | rad | -| 15 | joint_state | right_shoulder_yaw_joint | Right shoulder yaw joint angle | rad | -| 16 | joint_state | right_elbow_pitch_joint | Right elbow pitch joint angle | rad | -| 17 | joint_state | right_wrist_roll_joint | Right wrist roll joint angle | rad | -| 18 | joint_state | right_wrist_yaw_joint | Right wrist yaw joint angle | rad | -| 19 | joint_state | right_wrist_pitch_joint | Right wrist pitch joint angle | rad | -| 20 | joint_state | left_finger_l_joint | Left finger joint angle | rad | -| 21 | joint_state | right_finger_l_joint | Right finger joint angle | rad | - -##### 2. Joint Velocity (Index 22–43) -| Index | Source | Column Name | Physical Meaning | Unit | -| :--- | :--- | :--- | :--- | :--- | -| 22 | joint_state | folding_lower_joint_velocity | Folding lower joint angular velocity | rad/s | -| 23 | joint_state | folding_upper_joint_velocity | Folding upper joint angular velocity | rad/s | -| 24 | joint_state | waist_pitch_joint_velocity | Waist pitch joint angular velocity | rad/s | -| 25 | joint_state | torso_yaw_joint_velocity | Torso yaw joint angular velocity | rad/s | -| 26 | joint_state | head_yaw_joint_velocity | Head yaw joint angular velocity | rad/s | -| 27 | joint_state | head_pitch_joint_velocity | Head pitch joint angular velocity | rad/s | -| 28 | joint_state | left_shoulder_pitch_joint_velocity | Left shoulder pitch joint angular velocity | rad/s | -| 29 | joint_state | left_shoulder_roll_joint_velocity | Left shoulder roll joint angular velocity | rad/s | -| 30 | joint_state | left_shoulder_yaw_joint_velocity | Left shoulder yaw joint angular velocity | rad/s | -| 31 | joint_state | left_elbow_pitch_joint_velocity | Left elbow pitch joint angular velocity | rad/s | -| 32 | joint_state | left_wrist_roll_joint_velocity | Left wrist roll joint angular velocity | rad/s | -| 33 | joint_state | left_wrist_yaw_joint_velocity | Left wrist yaw joint angular velocity | rad/s | -| 34 | joint_state | left_wrist_pitch_joint_velocity | Left wrist pitch joint angular velocity | rad/s | -| 35 | joint_state | right_shoulder_pitch_joint_velocity | Right shoulder pitch joint angular velocity | rad/s | -| 36 | joint_state | right_shoulder_roll_joint_velocity | Right shoulder roll joint angular velocity | rad/s | -| 37 | joint_state | right_shoulder_yaw_joint_velocity | Right shoulder yaw joint angular velocity | rad/s | -| 38 | joint_state | right_elbow_pitch_joint_velocity | Right elbow pitch joint angular velocity | rad/s | -| 39 | joint_state | right_wrist_roll_joint_velocity | Right wrist roll joint angular velocity | rad/s | -| 40 | joint_state | right_wrist_yaw_joint_velocity | Right wrist yaw joint angular velocity | rad/s | -| 41 | joint_state | right_wrist_pitch_joint_velocity | Right wrist pitch joint angular velocity | rad/s | -| 42 | joint_state | left_finger_l_joint_velocity | Left finger joint angular velocity | rad/s | -| 43 | joint_state | right_finger_l_joint_velocity | Right finger joint angular velocity | rad/s | - -##### 3. Joint Effort (Index 44–65) -| Index | Source | Column Name | Physical Meaning | Unit | -| :--- | :--- | :--- | :--- | :--- | -| 44 | joint_state | folding_lower_joint_effort | Folding lower joint output torque | N·m | -| 45 | joint_state | folding_upper_joint_effort | Folding upper joint output torque | N·m | -| 46 | joint_state | waist_pitch_joint_effort | Waist pitch joint output torque | N·m | -| 47 | joint_state | torso_yaw_joint_effort | Torso yaw joint output torque | N·m | -| 48 | joint_state | head_yaw_joint_effort | Head yaw joint output torque | N·m | -| 49 | joint_state | head_pitch_joint_effort | Head pitch joint output torque | N·m | -| 50 | joint_state | left_shoulder_pitch_joint_effort | Left shoulder pitch joint output torque | N·m | -| 51 | joint_state | left_shoulder_roll_joint_effort | Left shoulder roll joint output torque | N·m | -| 52 | joint_state | left_shoulder_yaw_joint_effort | Left shoulder yaw joint output torque | N·m | -| 53 | joint_state | left_elbow_pitch_joint_effort | Left elbow pitch joint output torque | N·m | -| 54 | joint_state | left_wrist_roll_joint_effort | Left wrist roll joint output torque | N·m | -| 55 | joint_state | left_wrist_yaw_joint_effort | Left wrist yaw joint output torque | N·m | -| 56 | joint_state | left_wrist_pitch_joint_effort | Left wrist pitch joint output torque | N·m | -| 57 | joint_state | right_shoulder_pitch_joint_effort | Right shoulder pitch joint output torque | N·m | -| 58 | joint_state | right_shoulder_roll_joint_effort | Right shoulder roll joint output torque | N·m | -| 59 | joint_state | right_shoulder_yaw_joint_effort | Right shoulder yaw joint output torque | N·m | -| 60 | joint_state | right_elbow_pitch_joint_effort | Right elbow pitch joint output torque | N·m | -| 61 | joint_state | right_wrist_roll_joint_effort | Right wrist roll joint output torque | N·m | -| 62 | joint_state | right_wrist_yaw_joint_effort | Right wrist yaw joint output torque | N·m | -| 63 | joint_state | right_wrist_pitch_joint_effort | Right wrist pitch joint output torque | N·m | -| 64 | joint_state | left_finger_l_joint_effort | Left finger joint output torque | N·m | -| 65 | joint_state | right_finger_l_joint_effort | Right finger joint output torque | N·m | - -##### 4. End-Effector (Gripper) Pose (Index 66–79) -| Index | Source | Column Name | Physical Meaning | Unit | -| :--- | :--- | :--- | :--- | :--- | -| 66 | gripper_pose | left_gripper_x | Left gripper X position | m | -| 67 | gripper_pose | left_gripper_y | Left gripper Y position | m | -| 68 | gripper_pose | left_gripper_z | Left gripper Z position | m | -| 69 | gripper_pose | left_gripper_qx | Left gripper quaternion X component | - | -| 70 | gripper_pose | left_gripper_qy | Left gripper quaternion Y component | - | -| 71 | gripper_pose | left_gripper_qz | Left gripper quaternion Z component | - | -| 72 | gripper_pose | left_gripper_qw | Left gripper quaternion W component | - | -| 73 | gripper_pose | right_gripper_x | Right gripper X position | m | -| 74 | gripper_pose | right_gripper_y | Right gripper Y position | m | -| 75 | gripper_pose | right_gripper_z | Right gripper Z position | m | -| 76 | gripper_pose | right_gripper_qx | Right gripper quaternion X component | - | -| 77 | gripper_pose | right_gripper_qy | Right gripper quaternion Y component | - | -| 78 | gripper_pose | right_gripper_qz | Right gripper quaternion Z component | - | -| 79 | gripper_pose | right_gripper_qw | Right gripper quaternion W component | - | - -##### 5. Wheel Joint State (Index 80–88) -| Index | Source | Column Name | Physical Meaning | Unit | -| :--- | :--- | :--- | :--- | :--- | -| 80 | wheel_joint_state | wheel_front_left_position | Front left wheel angular position | rad | -| 81 | wheel_joint_state | wheel_front_right_position | Front right wheel angular position | rad | -| 82 | wheel_joint_state | wheel_rear_position | Rear wheel angular position | rad | -| 83 | wheel_joint_state | wheel_front_left_velocity | Front left wheel angular velocity | rad/s | -| 84 | wheel_joint_state | wheel_front_right_velocity | Front right wheel angular velocity | rad/s | -| 85 | wheel_joint_state | wheel_rear_velocity | Rear wheel angular velocity | rad/s | -| 86 | wheel_joint_state | wheel_front_left_effort | Front left wheel output torque | N·m | -| 87 | wheel_joint_state | wheel_front_right_effort | Front right wheel output torque | N·m | -| 88 | wheel_joint_state | wheel_rear_effort | Rear wheel output torque | N·m | - -### Robot Inference Interface -We provide a base Docker image for the robot's inference environment, in which you will need to implement two functions for your own model ``` -def load_model(self): - # TODO: implement model loading - pass - -def predict(self, sample: dict) -> np.ndarray: - # TODO: implement inference, return np.ndarray of shape (N, 25) - pass - -def inference_loop(self): - last_iner_time = time.time() - while self.running: - if self.prepare_in_progress or not self.enable_inference: - time.sleep(0.1) - continue - - tic = time.time() - sample = self.update_input() - prediction = self.predict(sample) +task{ID}_part{NNN}/ +├── _SUCCESS # packaging success marker (at part root) +├── data/ # frame-level state and action data (parquet shards) +│ └── chunk-{NNN}/ +│ └── file-{NNN}.parquet +├── meta/ # metadata files +│ ├── info.json +│ ├── tasks.parquet +│ ├── stats.json +│ ├── delivery_manifest.json +│ └── episodes/ +│ └── chunk-000/ +│ └── file-000.parquet # episode-level metadata +└── videos/ # three video streams, one subfolder each + ├── observation.images.pico/ + │ └── chunk-{NNN}/file-{NNN}.mp4 + ├── observation.images.left/ + │ └── chunk-{NNN}/file-{NNN}.mp4 + └── observation.images.right/ + └── chunk-{NNN}/file-{NNN}.mp4 ``` -Although the full action fields are recorded in the dataset, only robot joint positions are used as control commands(25-dimensional) during inference. The complete field definitions: - -| Index | Column Name | Physical Meaning | Unit | -| :--- | :--- | :--- | :--- | -| 0 | folding_lower_joint | Folding lower joint angle | rad | -| 1 | folding_upper_joint | Folding upper joint angle | rad | -| 2 | waist_pitch_joint | Waist pitch joint angle | rad | -| 3 | torso_yaw_joint | Torso yaw joint angle | rad | -| 4 | head_yaw_joint | Head yaw joint angle | rad | -| 5 | head_pitch_joint | Head pitch joint angle | rad | -| 6 | left_shoulder_pitch_joint | Left shoulder pitch joint angle | rad | -| 7 | left_shoulder_roll_joint | Left shoulder roll joint angle | rad | -| 8 | left_shoulder_yaw_joint | Left shoulder yaw joint angle | rad | -| 9 | left_elbow_pitch_joint | Left elbow pitch joint angle | rad | -| 10 | left_wrist_roll_joint | Left wrist roll joint angle | rad | -| 11 | left_wrist_yaw_joint | Left wrist yaw joint angle | rad | -| 12 | left_wrist_pitch_joint | Left wrist pitch joint angle | rad | -| 13 | right_shoulder_pitch_joint | Right shoulder pitch joint angle | rad | -| 14 | right_shoulder_roll_joint | Right shoulder roll joint angle | rad | -| 15 | right_shoulder_yaw_joint | Right shoulder yaw joint angle | rad | -| 16 | right_elbow_pitch_joint | Right elbow pitch joint angle | rad | -| 17 | right_wrist_roll_joint | Right wrist roll joint angle | rad | -| 18 | right_wrist_yaw_joint | Right wrist yaw joint angle | rad | -| 19 | right_wrist_pitch_joint | Right wrist pitch joint angle | rad | -| 20 | left_finger_l_joint | Left finger joint angle | rad | -| 21 | right_finger_l_joint | Right finger joint angle | rad | -| 22 | wheel_front_left_velocity | Front left wheel angular velocity | rad/s | -| 23 | wheel_front_right_velocity | Front right wheel angular velocity | rad/s | -| 24 | wheel_rear_velocity | Rear wheel angular velocity | rad/s | - -## UMI Data -UMI data is jointly sponsored by [crobotia](http://crobotia.com/) and PrimeBot Research Institute. The dataset is constructed in the standard **LeRobot v3.0**. The data description can be found in the README files of each batch. - -### Dataset Overview - -| Episode | `task_index` | Task | Frames | Duration | -| :--- | ---: | :--- | ---: | ---: | -| `episode_000000` | 0 | fold the red shirt | 1410 | 47 s | -| `episode_000001` | 1 | fold the black shirt | 1050 | 35 s | -| `episode_000002` | 2 | fold the yellow shirt | 870 | 29 s | -| `episode_000003` | 1 | fold the black shirt | 1050 | 35 s | -| `episode_000004` | 3 | fold the brown shirt | 1560 | 52 s | - -The dataset contains 5,940 frames and four unique tasks. All episodes are recorded at 30 FPS, and the ego videos have a resolution of 960 × 960. - -### Directory Structure - -```text -umi_sample_data_v21/ -├── data/chunk-000/ # Five episode Parquet files -├── videos/chunk-000/ -│ ├── observation.images.left_ego/ # Left-hand ego videos -│ └── observation.images.right_ego/ # Right-hand ego videos -├── meta/ -│ ├── info.json # Dataset and feature definitions -│ ├── tasks.jsonl # Task-to-task_index mapping -│ ├── episodes.jsonl # Episode lengths and tasks -│ ├── episodes_stats.jsonl # Per-episode statistics -│ └── calibration.json # Camera and IMU calibration -├── annotation/ # Episode-level and action-step annotations -└── imu/ # Left/right IMU data +Components: + +| Component | Location | Description | +| --------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | +| _SUCCESS | part root | Packaging success marker indicating a complete delivery. | +| data/ | part root | Frame-level data sharded as `chunk-{NNN}/file-{NNN}.parquet`, containing state, action, timestamp, and index fields. | +| meta/ | part root | Metadata folder with info.json, tasks.parquet, stats.json, delivery_manifest.json, and episode-level metadata parquet. | +| videos/ | part root | Video folder with `observation.images.pico/`, `observation.images.left/`, `observation.images.right/`, each sharded as `chunk-{NNN}/file-{NNN}.mp4`. | + +Files inside `meta/`: + +| File | Description | +| ----------------------------------- | -------------------------------------------------------------------------------------------------------------- | +| info.json | Package metadata: feature definitions, fps, split, and video parameters. | +| tasks.parquet | Task index table mapping task_index to the package task_id. | +| stats.json | Per-feature statistics (state / action, etc.): min, max, mean, std, count, q01, q99. | +| delivery_manifest.json | Delivery manifest: task_id, package_index, episode_count, duration_s, and the full list of source episode IDs. | +| episodes/chunk-000/file-000.parquet | Episode-level metadata (one row per episode): timestamps, source bag, robot ID, data/video shard indices. | + +Data and videos follow the LeRobot sharding convention: + +``` +data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet +videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4 ``` -### Dataset Fields +### Collection and Annotation -#### Camera Images +Data is collected with a UMI teleoperation rig. The collection and annotation pipeline is as follows: -| Dataset field | Source | -| :--- | :--- | -| `observation.images.left_ego` | Left wrist-mounted RGB ego camera, 960 × 960 | -| `observation.images.right_ego` | Right wrist-mounted RGB ego camera, 960 × 960 | +- Teleoperated collection: an operator uses handheld UMI grippers with a Pico headset and Tracker devices to perform bimanual garment manipulation; each continuous operation is recorded as one episode (source names like `episode_YYYYMMDD_hhmmss_YYYYMMDD_hhmmss` encode start/end timestamps). +- Visual recording: the pico camera and left/right wrist cameras record synchronized h264 video at 30 fps, written into the three subfolders under `videos/`. +- Pose annotation: 6-DoF poses of the main camera (Rigid_Pico) and the left/right anchor objects (Rigid_Tracker1/2) are calibrated by the optical/inertial tracking system and stored as position xyz + quaternion xyzw in observation.state under `data/`. +- Gripper and tactile annotation: gripper opening distance (real_distance) is read from the gripper sensors; dual-hand tactile skin pressure matrices (skin_sensor_left/right) are recorded per frame and flattened into vectors. +- Temporal alignment: all modalities are aligned to a unified 30 fps timeline; `timestamp`, `frame_index`, `episode_index`, and `index` make frames, videos, and episodes indexable. +- Task labeling: each package maps to one task_id (601/602/613/902), with `meta/tasks.parquet` mapping task_index to the task; the table above provides the semantic label for each task_id. +- Action labels: `action` is taken from the target opening distance (real_distance) of the left and right grippers, usable as supervision for imitation learning. +- QC and delivery: after each part is packaged, a `_SUCCESS` marker is written at the root, and `meta/delivery_manifest.json` records episode count, duration, and the source episode list for integrity verification. -#### Proprioception and Actions +### Visual Observations (Video) -Both `observation.state` and `action` are 16-dimensional and use the same field order: +| Video key (videos/ subfolder) | Resolution H x W x C | Codec | FPS | +| ----------------------------- | -------------------- | -------------- | --- | +| observation.images.pico | 768 x 1024 x 3 | h264 / yuv420p | 30 | +| observation.images.left | 1536 x 1920 x 3 | h264 / yuv420p | 30 | +| observation.images.right | 1536 x 1920 x 3 | h264 / yuv420p | 30 | -| Indices | Fields | Meaning | Unit | -| :--- | :--- | :--- | :--- | -| 0–2 | `left_x, left_y, left_z` | Left end-effector position | m | -| 3–6 | `left_qw, left_qx, left_qy, left_qz` | Left end-effector quaternion `(w, x, y, z)` | - | -| 7 | `left_gripper` | Left gripper opening angle | ° | -| 8–10 | `right_x, right_y, right_z` | Right end-effector position | m | -| 11–14 | `right_qw, right_qx, right_qy, right_qz` | Right end-effector quaternion `(w, x, y, z)` | - | -| 15 | `right_gripper` | Right gripper opening angle | ° | +All videos have no audio and are not depth maps. -`observation.state` represents the current-frame state. Except for the final frame, `action[t] = state[t+1]`. The final action retains the next-step target from the original capture sequence and therefore may differ from the final state of the episode. The left and right poses use independent coordinate systems and cannot be used directly to compute the relative distance or pose between the two hands. +### State Observation observation.state (791-dim, float32) -#### Index Fields +| Sub-field | Dim | Index range | Meaning | +| ------------------------ | --- | ----------- | ---------------------------------------------------------------------------- | +| main_camera_pose | 7 | 0-7 | World pose of the main camera (Rigid_Pico): position xyz + quaternion xyzw. | +| anchor_object_left_pose | 7 | 7-14 | World pose of the left-arm anchor object (AnchorObject_L / Rigid_Tracker1). | +| anchor_object_right_pose | 7 | 14-21 | World pose of the right-arm anchor object (AnchorObject_R / Rigid_Tracker2). | +| gripper_distance | 2 | 21-23 | Measured opening distance of the left and right grippers (real_distance). | +| left_hand_skin_left | 192 | 23-215 | Flattened tactile matrix of the left hand skin_sensor_left.csv. | +| left_hand_skin_right | 192 | 215-407 | Flattened tactile matrix of the left hand skin_sensor_right.csv. | +| right_hand_skin_left | 192 | 407-599 | Flattened tactile matrix of the right hand skin_sensor_left.csv. | +| right_hand_skin_right | 192 | 599-791 | Flattened tactile matrix of the right hand skin_sensor_right.csv. | -| Field | Type | Description | -| :--- | :--- | :--- | -| `timestamp` | float32 | Time within the episode, in seconds | -| `frame_index` | int64 | Zero-based frame index within the episode | -| `episode_index` | int64 | Episode index in the range 0–4 | -| `index` | int64 | Global frame index in the range 0–5,939 | -| `task_index` | int64 | Task identifier mapped by `meta/tasks.jsonl` | +Each pose is a 7-dim position xyz + quaternion xyzw representation; each tactile skin is a 192-dim vector flattened from a 2D pressure matrix. -#### Annotations and IMU +### Action action (2-dim, float32) -| File | Description | -| :--- | :--- | -| `annotation/episode_subtasks_*.jsonl` | Episode-level task, target-object, and success annotations | -| `annotation/action_steps_*.jsonl` | Fine-grained action-step segments | -| `imu/episode_*_{left,right}.csv` | Left/right timestamps, three-axis angular velocity, and three-axis acceleration | +| Sub-field | Dim | Index range | Meaning | +| ---------------- | --- | ----------- | ---------------------------------------------------------------------------------------------- | +| gripper_distance | 2 | 0-2 | Target opening distance of the left and right grippers (real_distance), roughly ranging 0-118. | -Annotation intervals use the half-open convention `[start_frame_index, end_frame_index)`: the start frame is included and the end frame is excluded. +### Other Frame Fields ---- +timestamp (float32), frame_index (int64), episode_index (int64), index (int64), task_index (int64). + +### Common Spec (identical across parts) + +| Item | Value | +| ---------------- | ----------------------- | +| codebase_version | v3.0 | +| robot_type | umi | +| Sampling FPS | 30 | +| State dim | 791 | +| Action dim | 2 | +| Video streams | 3 (pico / left / right) | + +### How to Load + +Each part is a complete LeRobot dataset (with data/, meta/, videos/) and can be loaded directly by its part root; to train jointly, concatenate multiple parts per task: + +```python +from lerobot.common.datasets.lerobot_dataset import LeRobotDataset + +dataset = LeRobotDataset("your-org/challenge_data", root="task601_part000") +print(dataset) +sample = dataset[0] +``` + +### License + +Unless otherwise noted, this dataset is released under the cc-by-sa-4.0 license. 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"episode_20260823_200510_20260823_200557", + "episode_20260823_200845_20260823_200929", + "episode_20260823_200936_20260823_201009", + "episode_20260823_201030_20260823_201102", + "episode_20260823_201257_20260823_201331", + "episode_20260823_201357_20260823_201451", + "episode_20260823_201536_20260823_201624", + "episode_20260823_201538_20260823_201632", + "episode_20260823_201625_20260823_201700", + "episode_20260823_201653_20260823_201747", + "episode_20260823_201810_20260823_201844", + "episode_20260823_201901_20260823_201934", + "episode_20260823_201953_20260823_202031", + "episode_20260823_202047_20260823_202139", + "episode_20260823_202328_20260823_202405", + "episode_20260823_202423_20260823_202459", + "episode_20260823_202710_20260823_202743", + "episode_20260823_202759_20260823_202834", + "episode_20260823_202854_20260823_202927", + "episode_20260823_202912_20260823_202944", + "episode_20260823_202933_20260823_203022", + "episode_20260823_202941_20260823_203014", + "episode_20260823_202955_20260823_203028", + "episode_20260823_203215_20260823_203300", + "episode_20260823_203226_20260823_203259", + "episode_20260823_203312_20260823_203345", + "episode_20260823_203318_20260823_203404", + "episode_20260823_203433_20260823_203504", + "episode_20260823_203440_20260823_203518", + "episode_20260823_203443_20260823_203533", + "episode_20260823_203531_20260823_203605", + "episode_20260823_203551_20260823_203626", + "episode_20260823_203617_20260823_203650" + ] +} \ No newline at end of file diff --git a/task601_part003/meta/info.json b/task601_part003/meta/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b1bdbe7712311991f2e921bad89a95649b3208f8 --- /dev/null +++ b/task601_part003/meta/info.json @@ -0,0 +1,1113 @@ +{ + "codebase_version": "v3.0", + "robot_type": "umi", + "total_episodes": 500, + "total_frames": 695830, + "total_tasks": 1, + "chunks_size": 1000, + "data_files_size_in_mb": 100, + "video_files_size_in_mb": 200, + "fps": 30, + "splits": { + "train": "0:500" + }, + "data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet", + "video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", + "features": { + "observation.images.pico": { + "dtype": "video", + "shape": [ + 768, + 1024, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 768, + "video.width": 1024, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + }, + "video_info": { + "video.height": 768, + "video.width": 1024, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + } + }, + "observation.images.left": { + "dtype": "video", + "shape": [ + 1536, + 1920, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + }, + "video_info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + } + }, + "observation.images.right": { + "dtype": "video", + "shape": [ + 1536, + 1920, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + }, + "video_info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + } + }, + "observation.state": { + "dtype": "float32", + "shape": [ + 791 + ], + "names": null, + "field_descriptions": { + "main_camera_pose": { + "description": "Main Camera（Rigid_Pico）的世界位姿，position xyz + quaternion xyzw。", + "dimensions": 7, + "indices": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "index_range": [ + 0, + 7 + ], + "source": "pico.Main Camera", + "fields": [ + "main_camera_position_x", + "main_camera_position_y", + "main_camera_position_z", + "main_camera_quaternion_x", + "main_camera_quaternion_y", + "main_camera_quaternion_z", + "main_camera_quaternion_w" + ] + }, + "anchor_object_left_pose": { + "description": "AnchorObject_L（Rigid_Tracker1，左腕）的世界位姿。", + "dimensions": 7, + "indices": [ + 7, + 8, + 9, + 10, + 11, + 12, + 13 + ], + "index_range": [ + 7, + 14 + ], + "source": "pico.AnchorObject_L", + "fields": [ + "anchor_object_left_position_x", + "anchor_object_left_position_y", + "anchor_object_left_position_z", + "anchor_object_left_quaternion_x", + "anchor_object_left_quaternion_y", + "anchor_object_left_quaternion_z", + "anchor_object_left_quaternion_w" + ] + }, + "anchor_object_right_pose": { + "description": "AnchorObject_R（Rigid_Tracker2，右腕）的世界位姿。", + "dimensions": 7, + "indices": [ + 14, + 15, + 16, + 17, + 18, + 19, + 20 + ], + "index_range": [ + 14, + 21 + ], + "source": "pico.AnchorObject_R", + "fields": [ + "anchor_object_right_position_x", + "anchor_object_right_position_y", + "anchor_object_right_position_z", + "anchor_object_right_quaternion_x", + "anchor_object_right_quaternion_y", + "anchor_object_right_quaternion_z", + "anchor_object_right_quaternion_w" + ] + }, + "gripper_distance": { + "description": "左右夹爪 real_distance。", + "dimensions": 2, + "indices": [ + 21, + 22 + ], + "index_range": [ + 21, + 23 + ], + "source": "gripper.distance", + "fields": [ + "left_gripper_real_distance", + "right_gripper_real_distance" + ] + }, + "left_hand_skin_left": { + "description": "左腕 skin_sensor_left.csv 的 Matrix 展平向量。", + "dimensions": 192, + "indices": [ + 23, 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"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4", + "features": { + "observation.images.pico": { + "dtype": "video", + "shape": [ + 768, + 1024, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 768, + "video.width": 1024, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + }, + "video_info": { + "video.height": 768, + "video.width": 1024, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + } + }, + "observation.images.left": { + "dtype": "video", + "shape": [ + 1536, + 1920, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + }, + "video_info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + } + }, + "observation.images.right": { + "dtype": "video", + "shape": [ + 1536, + 1920, + 3 + ], + "names": [ + "height", + "width", + "channels" + ], + "info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + }, + "video_info": { + "video.height": 1536, + "video.width": 1920, + "video.codec": "h264", + "video.pix_fmt": "yuv420p", + "video.is_depth_map": false, + "video.fps": 30, + "video.channels": 3, + "has_audio": false + } + }, + "observation.state": { + "dtype": "float32", + "shape": [ + 791 + ], + "names": null, + "field_descriptions": { + "main_camera_pose": { + "description": "Main Camera（Rigid_Pico）的世界位姿，position xyz + quaternion xyzw。", + "dimensions": 7, + "indices": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "index_range": [ + 0, + 7 + ], + "source": "pico.Main Camera", + "fields": [ + "main_camera_position_x", + "main_camera_position_y", + "main_camera_position_z", + "main_camera_quaternion_x", + "main_camera_quaternion_y", + "main_camera_quaternion_z", + "main_camera_quaternion_w" + ] + }, + "anchor_object_left_pose": { + "description": "AnchorObject_L（Rigid_Tracker1，左腕）的世界位姿。", + "dimensions": 7, + "indices": [ + 7, + 8, + 9, + 10, + 11, + 12, + 13 + ], + "index_range": [ + 7, + 14 + ], + "source": "pico.AnchorObject_L", + "fields": [ + "anchor_object_left_position_x", + "anchor_object_left_position_y", + "anchor_object_left_position_z", + "anchor_object_left_quaternion_x", + "anchor_object_left_quaternion_y", + "anchor_object_left_quaternion_z", + "anchor_object_left_quaternion_w" + ] + }, + "anchor_object_right_pose": { + 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