Ordering_Constrained_Black_Bin_20260714_135948

该数据集是一个机器人控制数据集，使用LeRobot创建，专注于机器人操作任务。数据集包含机器人的动作和观察数据：动作数据包括6个关节位置（肩部平移、肩部提升、肘部弯曲、手腕弯曲、手腕滚动和夹爪位置），观察数据包括相同的关节位置状态，以及两个摄像头图像（手腕摄像头和中间摄像头），均为视频格式，分辨率480x640，30帧/秒，编码为h264。数据集还包含时间戳、帧索引、episode索引、任务索引等元数据。数据集总共有5个episodes、2946帧、1个任务，数据以parquet文件存储，视频以mp4文件存储，机器人类型为so_follower，适用于机器人学习和控制研究。This dataset is a robot control dataset created using LeRobot, focusing on robot manipulation tasks. It includes robot action and observation data: action data consists of 6 joint positions (shoulder translation, shoulder elevation, elbow flexion, wrist flexion, wrist roll, and gripper position), while observation data includes the same joint position states, along with two camera images (wrist camera and middle camera), both in video format with a resolution of 480x640, 30 frames per second, encoded as h264. The dataset also contains metadata such as timestamps, frame indices, episode indices, and task indices. In total, there are 5 episodes, 2946 frames, and 1 task. Data is stored in parquet files, videos are stored in mp4 files, the robot type is so_follower, and it is suitable for robot learning and control research.

原始信息汇总

数据集概述

• 数据集名称: Ordering_Constrained_Black_Bin_20260714_135948
• 创建工具: 使用 LeRobot 创建
• 许可证: Apache-2.0
• 任务类别: 机器人 (Robotics)
• 标签: LeRobot

数据集结构

• 帧率 (FPS): 30
• 机器人类型: so_follower
• 总片段数 (Episodes): 5
• 总帧数: 2946
• 总任务数: 1
• 数据文件大小: 100 MB
• 视频文件大小: 200 MB

特征

• action:
  • 类型: float32
  • 形状: [6]
  • 名称: shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos
• observation.state:
  • 类型: float32
  • 形状: [6]
  • 名称: shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos
• observation.images.wrist:
  • 类型: 视频
  • 形状: [480, 640, 3]
  • 编码: h264, yuv420p, 30 fps
• observation.images.middle:
  • 类型: 视频
  • 形状: [480, 640, 3]
  • 编码: h264, yuv420p, 30 fps
• timestamp: float32, 形状 [1]
• frame_index: int64, 形状 [1]
• episode_index: int64, 形状 [1]
• index: int64, 形状 [1]
• task_index: int64, 形状 [1]

数据划分

• 训练集 (train): 0:5 (全部数据用于训练)

可视化

• 可在 可视化空间 中查看数据集。