In recent years, multimodal learning has become essential in robotic vision and information fusion, especially for understanding human behavior in complex environments. However, current methods struggle to fully leverage the textual modality, relying on supervised pretrained models, which limits semantic extraction in unsupervised robotic environments, particularly with significant modality loss. These methods also tend to be computationally intensive, leading to high resource consumption in real-world applications. To address these challenges, we propose the Multi Modal Mamba Enhanced Transformer (M3ET), a lightweight model designed for efficient multimodal learning, particularly on mobile platforms. By incorporating the Mamba module and a semantic-based adaptive attention mechanism, M3ET optimizes feature fusion, alignment, and modality reconstruction. Our experiments show that M3ET improves cross-task performance, with a 2.3 times increase in pretraining inference speed. In particular, the core VQA task accuracy of M3ET remains at 0.74, while the model's parameter count is reduced by 0.67. Although performance on the EQA task is limited, M3ET's lightweight design makes it well suited for deployment on resource-constrained robotic platforms.