record-test_20260711_110941

该数据集使用LeRobot创建，专注于机器人技术领域，包含机器人动作和观测数据。动作数据由6个浮点数表示关节位置（如肩部平移、肩部提升、肘部弯曲、腕部弯曲、腕部滚动、夹爪位置）；观测数据包括状态（由6个浮点数表示关节位置）和图像（来自前摄像头的视频，分辨率为480x640，帧率30帧/秒，非深度图）。此外，数据集还包括时间戳、帧索引、剧集索引、索引和任务索引等元数据。统计信息显示：总剧集数为1，总帧数为127，总任务数为1，机器人类型为so_follower。数据以parquet文件格式存储，总数据文件大小为100MB，视频文件大小为200MB。该数据集主要用于训练和评估机器人控制模型。This dataset was developed using LeRobot, focusing on the field of robotics, and contains robot action and observation data. The action data uses 6 floating-point numbers to represent joint positions, including shoulder translation, shoulder elevation, elbow flexion, wrist flexion, wrist roll, and gripper position. The observation data includes two components: the state (represented by 6 floating-point numbers for joint positions) and images (videos captured by the front-facing camera, with a resolution of 480×640, a frame rate of 30 frames per second, and non-depth maps). In addition, the dataset also includes metadata such as timestamps, frame indices, episode indices, data indices, and task indices. Statistical details show that the total number of episodes is 1, the total number of frames is 127, the total number of tasks is 1, and the robot type is so_follower. The data is stored in Parquet file format, with a total data file size of 100 MB and the video file size of 200 MB. This dataset is primarily used for training and evaluating robot control models.

原始信息汇总

• 许可证: Apache-2.0
• 任务类别: 机器人学
• 标签: LeRobot
• 创建工具: 使用 LeRobot 创建
• 数据集结构:
  • 总集数: 1
  • 总帧数: 127
  • 总任务数: 1
  • 帧率: 30 FPS
  • 机器人类型: so_follower
  • 数据分割: 训练集 (0:1)
  • 数据文件格式: Parquet (data//.parquet)
  • 视频文件格式: MP4
  • 数据文件大小: 100 MB
  • 视频文件大小: 200 MB
• 特征:
  • action (float32, 形状 [6]): 包含 shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos
  • observation.state (float32, 形状 [6]): 包含 shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos
  • observation.images.front (video, 形状 [480, 640, 3]): 视频格式，编码为 AV1，30 FPS，480x640 分辨率，3 通道
  • 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])