因子認識Mixture-of-Expertsと事前学習エンコーダによる組合せ汎化
Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
視覚ロボット操作において、照明や表面テクスチャなどの環境変化に対する汎化を向上させるため、因子認識MoEと事前学習エンコーダを統合したFAMEフレームワークを提案。3段階の訓練で複数因子の組合せ汎化を実現し、Meta-Worldで34%、実世界ピックアンドプレースで35%の改善。
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著者: Feihong Zhang, Guojian Zhan, Zeyu He, Yinuo Wang, Likun Wang, Tianze Zhu, Yao Lyu, Tao Zhang, Tinghao Yi, Wei You, Shengbo Eben Li
分類: cs.RO
原文アブストラクト
The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across complex environments with varying factors such as lighting and surface textures. To address this, we propose FAME, a framework that integrates a factor-aware mixture-of-experts (MoE) with a pretrained encoder to enhance generalization to environmental variations. FAME follows a three-stage training process: (1) policy warmup, where a diffusion policy is trained on standard-environment data with a frozen encoder; (2) factor-specific adapter training, where lightweight adapters inserted between the frozen encoder and the temporarily frozen policy are trained on customized datasets, each targeting a distinct environmental variation; and (3) joint fine-tuning, where a central router and the warmed policy are trained on mixed data to handle multiple factors jointly. FAME is ``factor-aware'' because the central router softly weights frozen factor-specific adapters as a dense MoE, enabling combinatorial generalization across multiple factors. Evaluations on the Meta-World benchmark show that FAME outperforms diffusion policy baselines by 34%. We further validate FAME in a real-world pick-and-place task using a compact model trained on newly collected data, where FAME achieves a 35% improvement in generalization under real-world variations.
