![[2602.16444] RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16444] RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation
Summary
RoboGene introduces a framework for automating the generation of diverse, physically plausible robotic manipulation tasks, addressing the challenges of data scarcity in robotics.
Why It Matters
The development of RoboGene is significant as it tackles the critical issue of limited real-world interaction data in robotics. By automating task generation, it enhances the training of robotic models, leading to improved performance and generalization in real-world applications. This advancement could facilitate more effective robotic solutions across various industries.
Key Takeaways
- RoboGene automates the generation of diverse manipulation tasks for robots.
- The framework incorporates diversity-driven sampling and self-reflection mechanisms.
- Real-world experiments show improved success rates for VLA models pre-trained with RoboGene.
- The approach addresses the limitations of manual task curation in robotics.
- RoboGene's datasets and metrics provide a foundation for future research in robotic task generation.
Computer Science > Robotics arXiv:2602.16444 (cs) [Submitted on 18 Feb 2026] Title:RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation Authors:Yixue Zhang (1,2), Kun Wu (1), Zhi Gao (3), Zhen Zhao (1), Pei Ren (1), Zhiyuan Xu (1), Fei Liao (1), Xinhua Wang (1), Shichao Fan (1,4), Di Wu (1,5), Qiuxuan Feng (1,5), Meng Li (1), Zhengping Che (1), Chang Liu (2), Jian Tang (1) ((1) Beijing Innovation Center of Humanoid Robotics, (2) The School of Advanced Manufacturing and Robotics, Peking University, (3) Beijing Institute of Technology, (4) The School of Mechanical Engineering and Automation, Beihang University, (5) State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University) View a PDF of the paper titled RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation, by Yixue Zhang (1 and 26 other authors View PDF HTML (experimental) Abstract:The pursuit of general-purpose robotic manipulation is hindered by the scarcity of diverse, real-world interaction data. Unlike data collection from web in vision or language, robotic data collection is an active process incurring prohibitive physical costs. Consequently, automated task curation to maximize data value remains a critical yet under-explored challenge. Existing manual methods are unscalable and biased toward common tasks, while off-the-shelf foundation models often hallucinate physic...
