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• RoboGene significantly outperforms state-of-the-art foundation models like GPT-4o and Gemini 2.5 Pro in generating diverse, physically plausible robotic manipulation tasks.result 90%
• RoboGene generates diverse, physically plausible manipulation tasks across single-arm, dual-arm, and mobile robots.method 90%
• VLA models pre-trained with RoboGene-generated data achieve higher success rates and superior generalization in real-world experiments.result 85%
• RoboGene employs a Least Frequently Used (LFU) strategy for diversity-driven sampling to cover under-explored task spaces.method 90%
• RoboGene's framework includes a self-reflection mechanism to ensure tasks meet physical constraints and novelty demands.method 90%
• The framework was validated by collecting datasets of 18k trajectories and introducing novel metrics for task quality, feasibility, and diversity.result 90%
• RoboGene may require significant adaptation for different robotic platforms.limitation 80%
• RoboGene can replace manual task design by human experts, which is often limited and biased.technical 85%

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