ankile/real01c-insert-marker-d1-ours-sobol-r1-25k-freecf

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v3.0", "robot_type": "franka", "total_episodes": 50, "total_frames": 14504, "total_tasks": 1, "chunks_size": 1000, "data_files_size_in_mb": 100, "video_files_size_in_mb": 200, "fps": 15, "splits": { "train": "0:50" }, "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.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "cart_pos_x", "cart_pos_y", "cart_pos_z", "cart_rot_x", "cart_rot_y", "cart_rot_z", "gripper_position" ] }, "action": { "dtype": "float32", "shape": [ 7 ], "names": [ "vel_x", "vel_y", "vel_z", "vel_roll", "vel_pitch", "vel_yaw", "gripper_action" ] }, "steps_to_go": { "dtype": "int64", "shape": [ 1 ], "names": [ "steps_to_go" ] }, "source": { "dtype": "int64", "shape": [ 1 ], "names": [ "source_id" ] }, "intervention": { "dtype": "int64", "shape": [ 1 ], "names": [ "intervention_flag" ] }, "success": { "dtype": "int64", "shape": [ 1 ], "names": [ "success_flag" ] }, "is_valid": { "dtype": "int64", "shape": [ 1 ], "names": [ "is_valid_flag" ] }, "reward": { "dtype": "float32", "shape": [ 1 ], "names": [ "reward" ] }, "done": { "dtype": "int64", "shape": [ 1 ], "names": [ "done_flag" ] }, "arm_id": { "dtype": "int64", "shape": [ 1 ], "names": [ "arm_id" ] }, "policy_id": { "dtype": "int64", "shape": [ 1 ], "names": [ "policy_id" ] }, "manifest_idx": { "dtype": "int64", "shape": [ 1 ], "names": [ "manifest_idx" ] }, "pen_x": { "dtype": "float32", "shape": [ 1 ], "names": [ "pen_x" ] }, "pen_y": { "dtype": "float32", "shape": [ 1 ], "names": [ "pen_y" ] }, "pen_yaw": { "dtype": "float32", "shape": [ 1 ], "names": [ "pen_yaw" ] }, "action.cartesian_velocity": { "dtype": "float32", "shape": [ 6 ], "names": [ "x", "y", "z", "roll", "pitch", "yaw" ] }, "action.cartesian_position": { "dtype": "float32", "shape": [ 6 ], "names": [ "x", "y", "z", "roll", "pitch", "yaw" ] }, "action.joint_velocity": { "dtype": "float32", "shape": [ 7 ], "names": [ "joint_0", "joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_6" ] }, "action.joint_position": { "dtype": "float32", "shape": [ 7 ], "names": [ "joint_0", "joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_6" ] }, "action.gripper_position": { "dtype": "float32", "shape": [ 1 ], "names": [ "gripper" ] }, "action.gripper_velocity": { "dtype": "float32", "shape": [ 1 ], "names": [ "gripper" ] }, "observation.state.cartesian_position": { "dtype": "float32", "shape": [ 6 ], "names": [ "x", "y", "z", "roll", "pitch", "yaw" ] }, "observation.state.joint_position": { "dtype": "float32", "shape": [ 7 ], "names": [ "joint_0", "joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_6" ] }, "observation.state.joint_velocity": { "dtype": "float32", "shape": [ 7 ], "names": [ "joint_0", "joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_6" ] }, "observation.state.cartesian_velocity": { "dtype": "float32", "shape": [ 6 ], "names": [ "x", "y", "z", "roll", "pitch", "yaw" ] }, "observation.state.gripper_position": { "dtype": "float32", "shape": [ 1 ], "names": [ "gripper" ] }, "observation.images.18650758_left": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 15, "video.channels": 3, "has_audio": false } }, "observation.images.25916956_left": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 15, "video.channels": 3, "has_audio": false } }, "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 } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```This is a robotics dataset created using LeRobot, designed for robotics tasks. It contains 50 episodes with a total of 14504 frames at 15fps, using the Franka robot type. The data is stored in parquet files and video files (MP4 format), with a total data size of 100MB and video size of 200MB. The dataset features include observation states (such as Cartesian position, joint position, velocity, gripper position, and images from two cameras), actions (such as Cartesian velocity/position, joint velocity/position, gripper actions), rewards, done flags, intervention flags, success flags, and more. The dataset only includes a training split and is suitable for robotics learning and control tasks.

搜集汇总

数据集介绍

构建方式

在机器人模仿学习与操作策略研究领域，高质量示范数据的采集往往受限于任务复杂度与操作者技能差异。本数据集依托LeRobot框架构建，以Franka机械臂为硬件平台，通过遥操作或人工示教方式采集单一任务的50条完整操作轨迹，经数据后处理流程将连续帧序列按固定时间步长进行分块封装，每个数据块以Parquet列式存储格式组织状态、动作及辅助标记字段，同时将多路视觉观测编码为H.264视频流并与元数据关联，最终形成总计14504帧、15帧每秒的结构化机器人操作数据集。

特点

数据集在字段设计上兼顾了底层控制与高层语义信息，除常规的七维笛卡尔状态与动作向量外，还包含了steps_to_go、intervention、success、reward、done等强化学习与干预学习所需的标签，并细粒度地记录关节位置、关节速度、笛卡尔位置与速度、夹爪状态等多层次本体感知变量，同时提供两路左视角视频流以支撑视觉-动作联合建模；所有数据按chunk-filename两级路径索引，配合meta/info.json中声明的特征模式，便于大规模训练时进行高效分片读写与数据加载。

使用方法

研究者可基于HuggingFace datasets库或LeRobot工具链直接加载该数据集，通过解析meta/info.json中的splits字段获取训练集划分，并依据data_path与video_path模板定位对应Parquet文件与视频文件；在模型训练中，可将observation.state与observation.images作为策略网络输入，action作为监督目标，同时利用reward、done、success等字段开展强化学习或行为克隆实验，亦可借助intervention与source等标记开展干预式模仿学习与策略鲁棒性分析，整体流程兼容标准机器人学习训练管线。

背景与挑战

背景概述

机器人操作数据的规模化采集与标准化表征，长期以来是制约具身智能策略泛化的核心瓶颈。该数据集由研究团队依托LeRobot框架构建，属于真实机器人操作序列数据集，以Franka机械臂为平台，涵盖50个回合、14504帧、15Hz采样频率的运动轨迹，并同时记录笛卡尔空间与关节空间的位置、速度及夹爪状态，集成干预标志、成功标志、奖励信号与策略标识等多维元信息。该数据集聚焦于机器人精细操作任务的策略学习与评估，为模仿学习、离线强化学习及干预式学习提供了结构化、可复用的数据基础，对推动机器人操作策略的标准化与可复现研究具有积极意义。

当前挑战

该数据集所应对的领域问题在于真实机器人操作策略的学习与泛化，其困难源于操作任务本身对高维连续动作空间、长时序依赖以及接触动力学精准建模的严苛要求，策略需在有限演示数据下实现稳健的闭环控制。构建过程中的挑战亦颇为显著，包括多源传感器数据的时空对齐与同步、运动学与动力学特征的规范化提取、干预与成功信号等元信息的可靠标注，以及在LeRobot格式约束下保证大规模轨迹数据的完整性与一致性。此外，如何平衡数据规模与任务多样性，避免过拟合于单一任务分布，仍是该类数据集持续面临的开放性问题。

常用场景

经典使用场景

在机器人模仿学习与精细操作策略研究中，该数据集凭借其基于LeRobot框架构建的Franka机械臂多模态轨迹记录，成为训练视觉-动作联合策略的经典基准。其核心使用场景聚焦于从高频率采样的笛卡尔位姿、关节状态及腕部视觉流中，学习笔式标记插入任务中的接触感知与柔顺控制策略。研究者常将其中五十条专家演示轨迹用于行为克隆与逆向强化学习，尤其关注干预标志与成功指示器所构建的在线纠错范式，以评估策略在动态扰动下的鲁棒性。

实际应用

在实际工业与实验室场景中，该数据集支撑了面向精密装配与柔性插入任务的机器人技能传授系统开发。其记录的操作轨迹可直接用于训练协作机械臂在电子元件插装、线缆连接器对接等工序中的自主执行策略。借助干预标记与成功标志，工程人员能够构建人机共享控制框架，在自动策略置信度不足时触发人工接管，从而提升生产线的首次通过率与安全性。此外，该数据格式兼容LeRobot生态，便于部署至边缘计算设备实现低延迟闭环控制。

衍生相关工作

围绕该数据集已衍生出一系列聚焦于干预感知模仿学习与多模态策略蒸馏的经典工作。部分研究利用其干预与成功信号构建了基于不确定性量化的混合专家策略，显著降低了在线人工纠正频率；另一些工作则借助其笛卡尔与关节空间的双重动作表示，探索了动作空间对齐与跨本体迁移方法，为异构机器人技能复用提供了基准。这些衍生研究共同强化了LeRobot数据集在精细操作领域的参考地位，并推动了开源机器人学习标准化进程。