Datasets:
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action list | observation.state list | timestamp float32 | frame_index int64 | episode_index int64 | index int64 | task_index int64 |
|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
GR00T 数据集:OmniReset 插孔(peg-insertion)· 可直接微调
用 OmniReset 的状态版 RL 专家(oracle) 在 Isaac Sim 里跑出的 UR5e + Robotiq-2F85 插孔演示, 已打包成 GR00T 版 LeRobot v2.1 格式,可直接喂给 NVIDIA GR00T N1.7 微调。
1000 条成功演示 · 复位状态按 40 / 30 / 20 / 10 配方 · 3 路相机(224×224)· 10 fps · ~98% 真实"搬运+插入" · 固定光照 · 物体颜色固定(适合 color-conditioned VLA)。
目录结构
.
├── README.md # 本文件
├── state_distribution.png # 状态分布 + peg 起始距离图
├── omnireset_peg_insertion/ # ★ 数据集(LeRobot v2.1)
│ ├── data/ videos/ meta/
│ └── meta/reset_type_per_episode.json # 每条 episode 的复位状态标签
└── finetune/ # omnireset_config.py + finetune.sh + setup_finetune_zh.md
数据分布(1000 条,配方 40/30/20/10)
四种复位状态(初始条件)—— 精确配方(通过"复用已有 demo + 只补采 ObjectAnywhereEEAnywhere
增量"实现;每条 episode 的标签见 omnireset_peg_insertion/meta/reset_type_per_episode.json):
| 复位状态 | 含义 | 数量 | 占比 |
|---|---|---|---|
ObjectAnywhereEEAnywhere |
peg 桌上、未抓取 → 接近+抓取+搬运+插入(最难/最完整) | 400 | 40% |
ObjectAnywhereEEGrasped |
peg 半空、已抓 → 搬运+插入 | 300 | 30% |
ObjectRestingEEGrasped |
peg 桌上、已抓 → 抬起+搬运+插入 | 200 | 20% |
ObjectPartiallyAssembledEEGrasped |
peg 已部分插入 → 补完 | 100 | 10% |
peg 到孔的起始距离: 均值 0.21 m、中位数 0.20 m、最大 ~0.58 m;右图为直方图。
40% 的 AnywhereEEAnywhere 使 peg 起始离孔更远、任务更完整。
说明:
AnywhereEEAnywhere400 条为新采集(精确标签);其余 3 类复用先前 1000 条均匀 采集的数据并按配方子采样(标签由轨迹几何推断,少量补采)。故 A 类标签精确,G/R/P 为近似。
生成耗时(单卡 A40)
- 稳定采集速度 ~35–40 条/分钟(修好动作尺度 + 关闭 sysid/OSC 增益随机化后,连最难的
AnywhereEEAnywhere成功率也 ~98%)。 - 本配方新采集:400 条 A(
16 分钟)+ 81 条 G/P(4 分钟)= 约 20 分钟;其余复用。 - HDF5 → LeRobot 转换(3000 段视频):~15 分钟。
快速开始
git clone https://github.com/NVIDIA/Isaac-GR00T && cd Isaac-GR00T && uv sync
bash /path/to/finetune/finetune.sh /path/to/omnireset_peg_insertion /tmp/gr00t_omnireset_peg
详见 finetune/setup_finetune_zh.md。
为什么能直接训练(格式对齐)
- LeRobot v2.1,
meta/info.json、meta/modality.json与 GR00T 官方 demo 数据集结构一致,LeRobotSingleDataset原生加载。 - 分辨率 224×224 = GR00T
image_target_size,无需缩放。 - 动作 7 维 = 相对末端(OSC)增量
[0:6]+ 二值夹爪[6];single_arm=RELATIVE、gripper=ABSOLUTE,契合 GR00T N1.7 相对末端动作空间。状态 7 维 = 6 关节角 + 夹爪。
生成代码与详细文档
配套仓库 yqi19/omnireset_reproduce (采集流程、格式转换、OSC 动作空间中文文档)。
来源
专家与资产:UW-Lab/uwlab-assets(OmniReset, ICLR 2026); 仿真:NVIDIA Isaac Sim / Isaac Lab;模型:NVIDIA GR00T N1.7。
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