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Object-Focus Actor for Data-efficient Robot Generalization Dexterous Manipulation

Main:8 Pages
6 Figures
Bibliography:3 Pages
3 Tables
Appendix:1 Pages
Abstract

Robot manipulation learning from human demonstrations offers a rapid means to acquire skills but often lacks generalization across diverse scenes and object placements. This limitation hinders real-world applications, particularly in complex tasks requiring dexterous manipulation. Vision-Language-Action (VLA) paradigm leverages large-scale data to enhance generalization. However, due to data scarcity, VLA's performance remains limited. In this work, we introduce Object-Focus Actor (OFA), a novel, data-efficient approach for generalized dexterous manipulation. OFA exploits the consistent end trajectories observed in dexterous manipulation tasks, allowing for efficient policy training. Our method employs a hierarchical pipeline: object perception and pose estimation, pre-manipulation pose arrival and OFA policy execution. This process ensures that the manipulation is focused and efficient, even in varied backgrounds and positional layout. Comprehensive real-world experiments across seven tasks demonstrate that OFA significantly outperforms baseline methods in both positional and background generalization tests. Notably, OFA achieves robust performance with only 10 demonstrations, highlighting its data efficiency.

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@article{li2025_2505.15098,
  title={ Object-Focus Actor for Data-efficient Robot Generalization Dexterous Manipulation },
  author={ Yihang Li and Tianle Zhang and Xuelong Wei and Jiayi Li and Lin Zhao and Dongchi Huang and Zhirui Fang and Minhua Zheng and Wenjun Dai and Xiaodong He },
  journal={arXiv preprint arXiv:2505.15098},
  year={ 2025 }
}
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