so101_dataset_test_20260721_183256
收藏Hugging Face2026-07-21 更新2026-07-21 收录
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https://huggingface.co/datasets/hwi12/so101_dataset_test_20260721_183256
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资源简介:
该数据集由LeRobot创建,专用于机器人技术领域。它包含9个总片段和3657个总帧,数据文件大小为100MB,视频文件大小为200MB。数据集的特征包括动作(action)和观察(observation),其中观察由状态(state)和两个视角的图像(wrist和side)组成,均为视频格式,分辨率为480x640,帧率为30fps。动作和状态都包含6个关节位置:肩部平移、肩部提升、肘部弯曲、腕部弯曲、腕部旋转和夹爪位置。数据集使用Apache 2.0许可证,适用于机器人控制和学习任务。
This dataset is created by LeRobot for the field of robotics. It contains 9 total segments and 3657 total frames, with a data file size of 100MB and a video file size of 200MB. The dataset features include action and observation, where observation consists of state and images from two perspectives (wrist and side), all in video format with a resolution of 480x640 and a frame rate of 30fps. Both action and state include 6 joint positions: shoulder translation, shoulder elevation, elbow flexion, wrist flexion, wrist rotation, and gripper position. The dataset uses the Apache 2.0 license and is suitable for robot control and learning tasks.
提供机构:
hwi12创建时间:
2026-07-21
原始信息汇总
数据集概述
- 数据集名称: so101_dataset_test_20260721_183256
- 许可证: Apache-2.0
- 任务类别: 机器人学(robotics)
- 标签: LeRobot
数据集结构
- 总集数(episodes): 9
- 总帧数(frames): 3657
- 总任务数(tasks): 1
- 数据帧率(fps): 30
- 机器人类型: so_follower
特征(Features)
| 特征名称 | 数据类型 | 形状 | 说明 |
|---|---|---|---|
action |
float32 | [6] | 6维关节位置控制指令(shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper) |
observation.state |
float32 | [6] | 6维关节位置观测(同action命名) |
observation.images.wrist |
video | [480, 640, 3] | 腕部摄像头视频(H.264编码,30fps,无音频) |
observation.images.side |
video | [480, 640, 3] | 侧方摄像头视频(H.264编码,30fps,无音频) |
timestamp |
float32 | [1] | 时间戳 |
frame_index |
int64 | [1] | 帧索引 |
episode_index |
int64 | [1] | 集索引 |
index |
int64 | [1] | 全局索引 |
task_index |
int64 | [1] | 任务索引 |
数据存储
- 数据文件路径:
data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet - 视频文件路径:
videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4 - 数据文件大小: 约100 MB
- 视频文件大小: 约200 MB
- 分块大小: 1000
数据集划分
- 训练集(train): 0-8集(共9集)




