OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912574169153536 |
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| author | Jawaid, Ahad Xiang, Yu |
| author_facet | Jawaid, Ahad Xiang, Yu |
| contents | Egocentric human videos provide scalable demonstrations for imitation learning, but existing corpora often lack either fine-grained, temporally localized action descriptions or dexterous hand annotations. We introduce OpenEgo, a multimodal egocentric manipulation dataset with standardized hand-pose annotations and intention-aligned action primitives. OpenEgo totals 1107 hours across six public datasets, covering 290 manipulation tasks in 600+ environments. We unify hand-pose layouts and provide descriptive, timestamped action primitives. To validate its utility, we train language-conditioned imitation-learning policies to predict dexterous hand trajectories. OpenEgo is designed to lower the barrier to learning dexterous manipulation from egocentric video and to support reproducible research in vision-language-action learning. All resources and instructions will be released at www.openegocentric.com. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation Jawaid, Ahad Xiang, Yu Computer Vision and Pattern Recognition Artificial Intelligence Robotics Egocentric human videos provide scalable demonstrations for imitation learning, but existing corpora often lack either fine-grained, temporally localized action descriptions or dexterous hand annotations. We introduce OpenEgo, a multimodal egocentric manipulation dataset with standardized hand-pose annotations and intention-aligned action primitives. OpenEgo totals 1107 hours across six public datasets, covering 290 manipulation tasks in 600+ environments. We unify hand-pose layouts and provide descriptive, timestamped action primitives. To validate its utility, we train language-conditioned imitation-learning policies to predict dexterous hand trajectories. OpenEgo is designed to lower the barrier to learning dexterous manipulation from egocentric video and to support reproducible research in vision-language-action learning. All resources and instructions will be released at www.openegocentric.com. |
| title | OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2509.05513 |