Core Viewpoint - The article discusses the AdaMoE architecture, which enhances the performance of Vision-Language-Action (VLA) models in robotic control by decoupling expert selection and weight distribution, leading to improved success rates in both simulation and real-world tasks [1][24]. Summary by Sections Research Background: The Three Dilemmas of VLA Models - Traditional VLA models face three main dilemmas: 1. Difficulty in improving performance due to high training costs, as collecting precise robotic data is resource-intensive [2]. 2. The challenge of real-time control, where dense models require all parameters to be activated, slowing down response times [3]. 3. The inefficiency of using Mixture of Experts (MoE) due to conflicts among experts, which hinders effective task execution [5]. Core Design: The Decoupling Magic of AdaMoE - AdaMoE's innovation lies in its ability to separate the roles of expert selection and performance evaluation, allowing each component to focus on its strengths rather than trying to solve all problems simultaneously [6]. Key Designs of AdaMoE - **Design 1**: Utilizes pre-trained weights to significantly reduce training costs by focusing on fine-tuning specialized skills rather than relearning basic actions [8]. - **Design 2**: Implements "sparse activation" and dual-module decoupling to balance capacity and efficiency while preventing conflicts among experts [9][10]. Key Findings: Advantages of Decoupling - The research team conducted extensive experiments revealing four key conclusions that highlight the superiority of AdaMoE: 1. Experts can effectively specialize in their tasks without interference, leading to improved performance [13]. 2. Decoupling responsibilities enhances performance compared to traditional coupling methods [15]. 3. Fewer, more specialized experts yield better results than a larger number of overlapping experts [19]. 4. Real-world scenarios benefit more from decoupling than simulated environments, with significant improvements in task success rates [22]. Experimental Results: Validation of AdaMoE - AdaMoE demonstrated superior performance across various benchmarks, achieving an average success rate of 96.0%, outperforming traditional models and other architectures [23]. Conclusion: The Breakthrough Significance of AdaMoE - AdaMoE not only improves performance but also provides a pathway for VLA models to operate effectively without excessive resource demands, emphasizing the importance of clear task specialization for both robots and humans [24][26].