实验室试剂配比训练数据集

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 collection is performed via handle-based teleoperation, covering multi-camera visual streams, robot body states, and operation commands. All sensor data is uniformly stored after being synchronized using timestamps. The data collection workflow consists of three core stages: task definition and teleoperation recording, data integrity verification, and annotation review, with triple quality control measures implemented including frame rate verification, logical consistency check, and file integrity verification. The collected data is archived in a structured manner based on task IDs and version numbers, while raw ROS2 topic messages are retained for traceability. This dataset complies with physical safety and data management standards, and is applicable to imitation learning algorithm training across diverse scenarios.

搜集汇总

背景与挑战

背景概述

该数据集专注于实验室试剂配比训练，包含40万条通过手柄遥操作采集的多相机视觉流和机器人状态数据，适用于模仿学习算法在溶液配制场景中的液体识别与定量移液任务。数据经过严格的质量管控和安全处理，确保不可逆性和隐私保护，旨在解决透明试剂液位识别困难、移液精度低等实际问题，支持智能机器人在光照稳定实验台下的自动化操作。