Expertise need not monopolize: Action-Specialized Mixture of Experts for
  Vision-Language-Action Learning
Authors: name: Weijie Shen, name: Yitian Liu, name: Yuhao Wu, name: Zhixuan Liang, name: Sijia Gu, name: Dehui Wang, name: Tian Nian, name: Lei Xu, name: Yusen Qin, name: Jiangmiao Pang, name: Xinping Guan, name: Xiaokang Yang, name: Yao Mu

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.