Core Insights - The article discusses significant advancements in visual-language-action models for robotic operations, highlighting the challenges faced in dynamic and cluttered environments, which hinder the deployment of existing models [2][4]. Research Background - Visual-language-action models have made notable progress in robotic operations through pre-trained visual language models that enable end-to-end mapping from language to action. However, two main bottlenecks limit their deployment in real-world scenarios: low computational efficiency and weak task grounding capabilities [2]. Key Innovations - Introduction of a semantic-guided dual-visual pruner that addresses visual redundancy through instruction-aware token filtering and geometric-aware aggregation, while maintaining semantic alignment [3]. Main Work Overall Framework Design - The framework processes real-time visual observations, robot state (e.g., joint angles, end-effector posture), and natural language instructions to predict future action sequences. It employs two parallel paths for visual input processing, culminating in an end-to-end pipeline for action mapping [4]. Visual Perception Redundancy - The general visual encoder processes all pixels uniformly, leading to background interference and environmental noise, which increases computational costs and dilutes attention on critical task cues [5]. Semantic Complementary Layered Fusion - A semantic complementary layered fusion mechanism integrates dense patch features with sparse semantic tokens, enhancing the alignment of instruction semantics with spatial structures [5]. Semantic Conditioned Action Coupler - The design reconstructs the mapping from visual to action, improving the efficiency and interpretability of action decoding by representing actions as semantically coherent types [5]. Experimental Results Efficiency Advantages - The model achieves a training cost reduction of 3.0 times, inference latency reduction of 2.7 times, and visual token compression of 8-16 times, significantly enhancing throughput [14]. Real-World Performance - In long-range tasks, the model's success rate reaches 77.8%, surpassing the OpenVLA-OFT model by 22.2%, demonstrating strong generalization capabilities [14]. Ablation Studies - The dual-pruning combination of the SD-Pruner enhances success rates by 2.1%-5.2%, achieving optimal performance and efficiency balance at an 8× sparsification ratio [16].