専門性は独占する必要なし：視覚-言語-行動学習のための行動特化型Mixture of Experts
Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning
事前学習済みVLAモデルの重みを継承し、フィードフォワード層をスパースなMoE層に置き換えて拡張するAdaMoEを提案。専門家選択と重み付けを分離することで協調的な専門家利用を実現し、計算効率を保ちつつ性能を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Weijie Shen, Yitian Liu, Yuhao Wu, Zhixuan Liang, Sijia Gu, Dehui Wang, Tian Nian, Lei Xu, Yusen Qin, Jiangmiao Pang, Xinping Guan, Xiaokang Yang, Yao Mu
分類: cs.RO, cs.AI
原文アブストラクト
Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presents several critical challenges: (1) Training new VLA models from scratch demands substantial computational resources and extensive datasets. Given the current scarcity of robot data, it becomes particularly valuable to fully leverage well-pretrained VLA model weights during the scaling process. (2) Real-time control requires carefully balancing model capacity with computational efficiency. To address these challenges, We propose AdaMoE, a Mixture-of-Experts (MoE) architecture that inherits pretrained weights from dense VLA models, and scales up the action expert by substituting the feedforward layers into sparsely activated MoE layers. AdaMoE employs a decoupling technique that decouples expert selection from expert weighting through an independent scale adapter working alongside the traditional router. This enables experts to be selected based on task relevance while contributing with independently controlled weights, allowing collaborative expert utilization rather than winner-takes-all dynamics. Our approach demonstrates that expertise need not monopolize. Instead, through collaborative expert utilization, we can achieve superior performance while maintaining computational efficiency. AdaMoE consistently outperforms the baseline model across key benchmarks, delivering performance gains of 1.8% on LIBERO and 9.3% on RoboTwin. Most importantly, a substantial 21.5% improvement in real-world experiments validates its practical effectiveness for robotic manipulation tasks.
