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arXiv · 2610.40207

RoboAtom: Large-scale Atomic-Action Supervision for Robot Learning

Xueyuan Wei·Xuanxu Wang·Xudong Li·Yuping He·Wanshui Gan·Jiaqi Peng·Wenqi Guo·Jianjun Zhou·Junting Dong·Chunhua Shen·Haoyu Guo·Mulin Yu·Tao Lu
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Abstract

Fine-grained action annotations can support skill reuse and compositional generalization in robot policies. However, robot datasets typically provide task-level instructions without subtask annotations. Although some include subtask labels, inconsistent formats and conventions hinder cross-source joint training. We propose RoboAtom-Data, which adds unified, time-aligned atomic-action annotations to existing data from multiple sources, covering 1.45 million trajectories, 6.88 million skill-labeled segments, and 16,882.1 hours. Our pipeline combines a unified protocol, large-scale manual annotation, propagation across repeated executions with the same ordered skill sequence, and multi-stage quality control. We evaluate the dataset through automatic atomic-action annotation and robot control. We construct RoboAtom-Bench to standardize evaluation of skill recognition and temporal boundary localization. Trained on the annotated data, RoboAtom-Anno achieves better automatic annotation performance than all evaluated open- and closed-source models on both RoboAtom-Bench and the external WGO-Bench. For robot control, atomic-action supervision paired with hierarchical execution improves task success over task-level baselines on LIBERO-Long, LIBERO-Plus, and RoboTwin. We will release the data, benchmark, model, and construction pipeline to support further research on skill-level robot learning.

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Xueyuan Wei, Xuanxu Wang, Xudong Li, Yuping He, Wanshui Gan, Jiaqi Peng, Wenqi Guo, Jianjun Zhou, Junting Dong, Chunhua Shen, Haoyu Guo, Mulin Yu, Tao Lu. 2026-10-29. RoboAtom: Large-scale Atomic-Action Supervision for Robot Learning. https://arxiv.org/abs/2610.40207

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