文具整理训练数据集

内部数据采集通过手柄遥操作完成，覆盖多相机视觉流、机器人本体状态及操作指令，各传感器数据经时间戳同步后统一存储。采集流程包含任务定义、遥操作录制、数据完整性校验、标注审核三个核心环节，并实施帧率验证、逻辑一致性检查及文件完整性校验三重质量管控。数据按任务ID与版本号结构化归档，保留原始ROS2话题消息以备回溯，符合物理安全与数据管理规范，适用于多场景下的模仿学习算法训练。

Internal data is collected via handle-based teleoperation, covering multi-camera visual streams, robot body states and operation commands. All sensor data is uniformly stored after being synchronized via timestamps. The data collection workflow includes three core stages: task definition, teleoperation recording, data integrity verification and annotation review, and implements three-tier quality control including frame rate verification, logical consistency check and file integrity validation. The data is structured and archived based on task IDs and version numbers, with original ROS2 topic messages preserved for traceability. It complies with physical safety and data management specifications, and is suitable for imitation learning algorithm training across multiple scenarios.

搜集汇总

背景与挑战

背景概述

该数据集是一个专门用于智能机器人在学校或办公场景中进行文具整理任务的训练数据集，包含40万条通过手柄遥操作采集的多传感器数据，每天更新。它针对笔、尺、橡皮等杂乱小件文具的识别与分类问题设计，适用于模仿学习算法训练，并通过严格的质量管控和隐私保护处理确保数据安全可靠。