Linking Visual Quality to Task Performance in Surgical Robotics

Abstract

Robotic surgical systems rely on visual input to perform critical tasks such as tool manipulation and gesture recognition, where image quality directly affects performance. However, conventional image quality assessment (IQA) methods fail to reflect how degradations impact robotic performance. We propose Task-Driven Embodied Vision Quality Assessment (TD-EVQA), a novel framework that evaluates image quality based on its effect on downstream robotic task performance rather than human perception. To support this, we introduce SurgDegrade, a large-scale benchmark of degraded surgical images with quality labels derived from performance degradation in robotic tasks. By simulating real-world degradations at varying levels, we establish a fine-grained, task-aware quality supervision signal. To learn this quality-performance mapping, TD-EVQA adapts frozen vision-language foundation models via prompt tuning. The model incorporates static context prompts, instance-conditioned adaptation, and knowledge-guided regularization to infer task-relevant quality scores. Experiments show that TD-EVQA surpasses existing IQA methods in predicting performance degradation under visual distortion. Our approach redefines visual quality from a functional perspective, enhancing safety and reliability in robotic surgical systems.