The field of robotics is moving towards greater integration of multimodal sensing and reasoning, enabling more effective and generalizable visuomotor control policies. Recent advances in vision-language-action models have shown promise in addressing the data scarcity challenge in robotics, but deploying these models on resource-constrained systems remains a significant hurdle. To overcome this, researchers are exploring novel approaches to improve the efficiency and inference speed of these models, such as eliminating autoregressive requirements and leveraging small language models. Additionally, there is a growing focus on incorporating tactile feedback into these systems, which is crucial for effective interaction with the physical world. Other notable trends include the development of data augmentation frameworks for editing 4D robotic multi-view images and the introduction of new training paradigms for end-to-end vision-language-action models. Noteworthy papers include EdgeVLA, which achieves real-time performance on edge devices, and InstructVLA, which preserves the flexible reasoning of large vision-language models while delivering leading manipulation performance. VLA-Touch is also notable for its dual-level integration of tactile feedback, which improves task planning efficiency and execution precision.