# Semantically Structured Mixture-of-Experts for Compositional Robotic Manipulation Source: [Semantically Structured Mixture-of-Experts for Compositional Robotic Manipulation]() Collection index: [llms.txt]() Data updated: 2026-05-26T05:00:05.667Z Authors: Chengyu Deng, Guanqi Chen, Yizhou Chen, Zejia Liu, Zhiwen Ruan, Guanhua Chen, Jia Pan Published: 2026-05-22T10:38:59.000Z ## Abstract Diffusion-based policies have established a new standard for precise robotic manipulation but face a critical scalability bottleneck: high-performance models are computationally expensive, while lightweight alternatives often fail to generalize across diverse multi-task environments. Mixture-of-Experts (MoE) architectures offer a promising path to efficiency by activating only a subset of parameters. However, existing MoE routing mechanisms typically rely on low-level noise or latent statistics, ignoring the compositional nature of manipulation tasks. This can fragment reusable behaviors across experts, limiting interpretability and transferability. We introduce Semantically Structured Mixture-of-Experts Diffusion Policy (SMoDP) for compositional robotic manipulation, a framework that grounds expert specialization in semantic task structure. SMoDP leverages a lightweight, inference-time skill predictor, supervised by offline annotations from Vision-Language Models (VLMs), to route action chunks to experts specialized for specific behavioral phases. To ensure robust assignment, we propose a dual contrastive alignment strategy that grounds multi-modal observations in language-defined skill semantics (Inter-modal) while enforcing routing consistency across visually distinct but functionally related behaviors (Intra-modal). Our approach outperforms representative diffusion and MoE-based baselines on multi-task benchmarks with significantly improved parameter efficiency and demonstrates effective compositional transfer to novel tasks through parameter-efficient fine-tuning. Project website: https://deng-cy20.github.io/SMoDP/ ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Robot Manipulation on LIBERO GOAL OOD (Success Rate)](): Robot Manipulation; LIBERO GOAL OOD; Success Rate: 66. - [Bimanual Robot Manipulation on Real-world Bimanual Manipulation Tasks multi-task learning settings](): Bimanual Robot Manipulation; Real-world Bimanual Manipulation Tasks multi-task learning settings; Hang Up Tape Success Rate: 19. - [Robot Manipulation on LIBERO 10](): Robot Manipulation; LIBERO-10; Success Rate: 84. ## Original sources and implementation [Original paper]() [PDF]()