毯子整理机器人数据集

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。This internal dataset is collected through joystick teleoperation, covering multi-camera visual streams, the robot's on-board operational status, and control commands. All sensor data is uniformly stored following timestamp-based synchronization. The data collection workflow consists of three core phases: task definition, teleoperation recording, as well as integrated data integrity verification and annotation review. Three-tier quality control measures are implemented throughout the process, 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, with original ROS2 topic messages retained for retrospective traceability. This dataset complies with physical safety and data management regulations, and is suitable for imitation learning algorithm training across various scenarios.

搜集汇总

背景与挑战

背景概述

该数据集专注于毯子整理机器人的模仿学习，包含40万条通过手柄遥操作采集的多传感器数据，覆盖视觉流和机器人状态，适用于智能家居和酒店场景的柔性纺织品识别与形变控制算法训练。数据经过严格质量管控和结构化归档，并采用多层非可逆模型处理以确保隐私安全，支持每天更新，为服务消费机器人制造领域提供标准化、可回溯的训练资源。