Title: Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning
Primary area: applications to robotics, autonomy, planning
Keywords: Mixture of Experts; VLA; Load Balancing; Robotics

Abstract: 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; (2) Real-time control requires carefully balancing model capacity with computational efficiency. Given the current scarcity of robot data, it becomes particularly valuable to fully leverage well-pretrained VLA model weights during the scaling process.  To address these challenges, We propose AdaMoE, a Mixture-of-Experts (MoE) architecture that inherits pretrained weights from dense VLA models, scales up the action expert, and employs a decoupling technique where expert selection and weighting are independently controlled to effectively leverage each expert's capabilities. 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.