YAML Metadata Warning:The task_categories "action-recognition" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

YAML Metadata Warning:The task_categories "point-cloud" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Pico Robotics Dataset · Annotated Edition

Pico + Tracker 机器人学习数据集 — 粗标版

在进阶版的基础上，增加动作分段粗标注（segments.json），每个片段都经过动作类型划分，可直接用于动作识别、行为理解等下游任务。

🔒 本仓库为门禁（Gated）数据集，申请通过后即可下载。

👉 点击这里填写表单，自动获取访问权限。

🎯 三版本对比

版本	内容	访问方式
Basic	去畸变双路视频 + 逐帧深度图 + 双路麦克风音频 + ~1kHz 6DoF 头部位姿 + 相机标定	🟢 公开下载
Advanced	基础版全部 + 位姿对齐立体点云(.npz) + 21 关节手部 + 世界坐标点云 + MCAP/Foxglove 可视化	🔒 申请访问
Annotated（本仓库）	进阶版全部 + 动作分段粗标 + segments.json 标注文件	🔒 申请访问

📁 目录结构

sample_01/
├── head_left_camera_undistorted.mp4    # 左摄像头去畸变视频
├── head_right_camera_undistorted.mp4   # 右摄像头去畸变视频
├── undistort_camera.json               # 相机标定参数
├── video_index.json                    # 帧索引 + 6DoF 位姿
├── depth/                              # 逐帧深度图
├── point_cloud/                        # 立体点云 (.npz)
├── hand_landmarks/                     # 21 关节手部关键点
├── world_pose/                         # 世界坐标位姿对齐
├── recording.mcap                      # MCAP 格式
└── segments.json                       # ⭐ 动作分段粗标注

🏷️ segments.json 标注格式

{
  "video_id": "sample_01",
  "total_frames": 12500,
  "segments": [
    {
      "label": "reaching",
      "start_frame": 0,
      "end_frame": 842,
      "start_time": 0.0,
      "end_time": 28.07
    },
    {
      "label": "grasping",
      "start_frame": 843,
      "end_frame": 1520,
      "start_time": 28.1,
      "end_time": 50.67
    }
  ]
}

动作标签涵盖抓取、放置、移动、操作、观察等常见机器人交互动作。

🚀 快速读取标注

import json

with open("sample_01/segments.json") as f:
    data = json.load(f)

print(f"视频总帧数: {data['total_frames']}")
print(f"分段数: {len(data['segments'])}")
for seg in data["segments"]:
    print(f"  {seg['label']}: frame {seg['start_frame']}-{seg['end_frame']}")

📜 许可证

本数据集采用 CC BY 4.0 协议发布。使用时请注明：

Pico Robotics Dataset by skycn110, licensed under CC BY 4.0

📬 联系与合作

数据咨询 / 商业合作 / 定制标注：skycn110@gmail.com
数据申请 / 更多信息：填写表单

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