tablewares_sort_ik_auto1
收藏资源简介:
该数据集是一个机器人学习数据集,使用LeRobot工具创建,专注于机器人控制任务。数据集基于openarm_follower机器人类型,包含1个任务、1个剧集和1206帧数据。数据以parquet文件格式存储,视频文件为mp4格式,帧率为30fps。数据集的特征包括动作(8维关节位置和夹爪位置)、观察状态(8维关节位置和夹爪位置)、来自前部和腕部摄像头的图像观察(分辨率720x1280,3通道),以及时间戳、帧索引、剧集索引等辅助信息。这些数据用于训练和评估机器人控制模型,适用于强化学习或模仿学习场景。
This dataset is a robot learning dataset created using the LeRobot tool, focusing on robot control tasks. It is based on the openarm_follower robot type and contains 1 task, 1 episode, and 1206 frames of data. The data is stored in parquet file format, with video files in mp4 format at a frame rate of 30fps. The dataset features include actions (8-dimensional joint positions and gripper positions), observation states (8-dimensional joint positions and gripper positions), image observations from front and wrist cameras (resolution 720x1280, 3 channels), as well as auxiliary information such as timestamps, frame indices, and episode indices. This data is used for training and evaluating robot control models, suitable for reinforcement learning or imitation learning scenarios.
数据集概述
- 数据集名称: tablewares_sort_ik_auto1
- 许可证: Apache-2.0
- 任务类别: 机器人学 (robotics)
- 标签: LeRobot
- 创建工具: 基于 LeRobot 创建
数据集规模与结构
- 总片段数 (Episodes): 1
- 总帧数 (Frames): 1206
- 总任务数 (Tasks): 1
- 帧率 (FPS): 30
- 数据文件大小: 100 MB
- 视频文件大小: 200 MB
- 数据分块大小 (Chunks): 1000
- 数据拆分: 仅训练集 (train: 0:1)
机器人类型
- 机器人型号: openarm_follower
数据特征
| 特征名 | 数据类型 | 形状 | 说明 |
|---|---|---|---|
| action | float32 | [8] | 动作:7个关节位置 + 1个夹爪位置 |
| observation.state | float32 | [8] | 状态观测:7个关节位置 + 1个夹爪位置 |
| observation.images.front | video | [720, 1280, 3] | 前方摄像头视频,720x1280,AV1编码,30fps |
| observation.images.wrist | video | [720, 1280, 3] | 腕部摄像头视频,720x1280,AV1编码,30fps |
| timestamp | float32 | [1] | 时间戳 |
| frame_index | int64 | [1] | 帧索引 |
| episode_index | int64 | [1] | 片段索引 |
| index | int64 | [1] | 全局索引 |
| task_index | int64 | [1] | 任务索引 |
数据存储路径
- 数据文件:
data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet - 视频文件:
videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4



