冷水壶开盖训练数据集

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 joystick-based teleoperation, covering multi-camera visual streams, robot body states and operation commands. All sensor data is uniformly stored after being synchronized with timestamps. The data collection workflow includes three core stages: task definition, teleoperation recording, data integrity verification and annotation review. Triple-tiered quality control is implemented, covering frame rate verification, logical consistency inspection and file integrity verification. The data is archived in a structured manner based on task IDs and version numbers, with original ROS2 topic messages preserved for traceability. This dataset complies with physical safety and data management specifications, and is suitable for imitation learning algorithm training across various scenarios.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于机器人模仿学习的冷水壶开盖训练数据集，通过遥操作采集多相机视觉流、机器人状态和操作指令，并经过时间戳同步。数据集实施严格的质量管控，包括帧率验证和逻辑一致性检查，确保数据完整性；数据按任务ID和版本号结构化归档，保留原始ROS2消息，适用于多场景下的算法训练。