Seed2Scale: 小規模・大規模モデルの協調とマルチモーダル評価による身体化AIの自己進化型データエンジン
Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation
少数のシードデモから始め、小型モデルでデータ収集し大型モデルで評価する自己進化型データエンジンを提案し、データ不足とモデル崩壊を克服して成功率を大幅に向上させた。
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1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Cong Tai, Zhaoyu Zheng, Haixu Long, Hansheng Wu, Zhengbin Long, Haodong Xiang, Rong Shi, Zhuo Cui, Shizhuang Zhang, Gang Qiu, He Wang, Ruifeng Li, Biao Liu, Zhenzhe Sun, Tao Shen
分類: cs.RO
原文アブストラクト
Existing data generation methods suffer from exploration limits, embodiment gaps, and low signal-to-noise ratios, leading to performance degradation during self-iteration. To address these challenges, we propose Seed2Scale, a self-evolving data engine that overcomes the data bottleneck through a heterogeneous synergy of "small-model collection, large-model evaluation, and target-model learning". Starting with as few as four seed demonstrations, the engine employs the lightweight Vision-Language-Action model, SuperTiny, as a dedicated collector, leveraging its strong inductive bias for robust exploration in parallel environments. Concurrently, a pre-trained Vision-Language Model is integrated as a Verifer to autonomously perform success/failure judgment and quality scoring for the massive generated trajectories. Seed2Scale effectively mitigates model collapse, ensuring the stability of the self-evolution process. Experimental results demonstrate that Seed2Scale exhibits signifcant scaling potential: as iterations progress, the success rate of the target model shows a robust upward trend, achieving a performance improvement of 131.2%. Furthermore, Seed2Scale signifcantly outperforms existing data augmentation methods, providing a scalable and cost-effective pathway for the large-scale development of Generalist Embodied AI. Project page: https://terminators2025.github.io/Seed2Scale.github.io
