robocurve/gr00t-n1.7-yam-molmoact2
Robotics • 3B • Updated • 153 • 1
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This dataset was created using LeRobot.
MolmoAct2-BimanualYAM Dataset
This repository is the merged ckpt / merged LeRobot dataset artifact for the MolmoAct2-BimanualYAM Dataset, a large-scale collection of bimanual robot manipulation demonstrations collected for MolmoAct2. Across the full collection, MolmoAct2-BimanualYAM contains more than 720 hours of training demonstrations spanning diverse tabletop manipulation tasks.
Language Annotations
This dataset includes annotated language instructions in meta/tasks_annotated.parquet. The file is indexed by episode_index and has a task column containing our per-episode annotated instruction.
The standard LeRobot loader resolves a frame's language instruction through task_index: each data row stores a task_index, which is looked up in meta/tasks.parquet. When you use these annotations, load meta/tasks_annotated.parquet and look up the current episode_index instead. If no valid annotated row is available, fall back to the standard LeRobot task.
Normalization Statistics
For MolmoAct2 training, we did not train directly from this single merged LeRobot repository. The training pipeline used a custom dataloader that takes multiple LeRobot datasets as separate sources and composes them at load time.
Because the training mixture can be defined from arbitrary combinations of source datasets, quantile-based normalization statistics are estimated by taking weighted averages of per-source dataset quantiles, instead of recomputing exact global quantiles for every possible dataset mixture. This makes it practical to reuse source-level statistics across different combinations of datasets.
Because quantiles are nonlinear, weighted averages of per-source quantiles are not identical to exact quantiles computed from the fully merged dataset, so this approximation can introduce some bias. In practice, this normalization scheme worked well for MolmoAct2 training.
Visualization
Use the MolmoAct2 LeRobot Visualizer to inspect episodes from this dataset, including camera streams, robot state, actions, and language annotations.
Citation
@misc{fang2026molmoact2actionreasoningmodels,
title={MolmoAct2: Action Reasoning Models for Real-world Deployment},
author={Haoquan Fang and Jiafei Duan and Donovan Clay and Sam Wang and Shuo Liu and Weikai Huang and Xiang Fan and Wei-Chuan Tsai and Shirui Chen and Yi Ru Wang and Shanli Xing and Jaemin Cho and Jae Sung Park and Ainaz Eftekhar and Peter Sushko and Karen Farley and Angad Wadhwa and Cole Harrison and Winson Han and Ying-Chun Lee and Eli VanderBilt and Rose Hendrix and Suveen Ellawela and Lucas Ngoo and Joyce Chai and Zhongzheng Ren and Ali Farhadi and Dieter Fox and Ranjay Krishna},
year={2026},
eprint={2605.02881},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2605.02881},
}
Dataset Structure
{
"codebase_version": "v3.0",
"robot_type": "bi_yam_follower",
"total_episodes": 32246,
"total_frames": 76046658,
"total_tasks": 34,
"chunks_size": 1000,
"data_files_size_in_mb": 0.001,
"video_files_size_in_mb": 0.001,
"fps": 30,
"splits": {
"train": "0:32246"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
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"names": [
"left_joint_0.pos",
"left_joint_1.pos",
"left_joint_2.pos",
"left_joint_3.pos",
"left_joint_4.pos",
"left_joint_5.pos",
"left_gripper.pos",
"right_joint_0.pos",
"right_joint_1.pos",
"right_joint_2.pos",
"right_joint_3.pos",
"right_joint_4.pos",
"right_joint_5.pos",
"right_gripper.pos"
],
"shape": [
14
]
},
"observation.state": {
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"left_gripper.pos",
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"right_joint_1.pos",
"right_joint_2.pos",
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],
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]
},
"observation.images.right": {
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],
"names": [
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},
"observation.images.top": {
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"video.channels": 3,
"has_audio": false
}
},
"timestamp": {
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"shape": [
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"names": null
},
"frame_index": {
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},
"episode_index": {
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"index": {
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},
"task_index": {
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}
}
}
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