Reinforced Multi-Path Graph Attention Network (MPGA-RL-Net) for Adaptive Robotic Vision with Task-Specific Region Prioritization

Robotic vision plays a crucial role in enabling autonomous systems to perceive, understand, and interact with complex environ- ments. However, accurately segmenting and prioritizing visual regions of interest in dynamic scenes is challenging due to variations in object shapes, sizes, and spatial relationships. Traditional methods, such as Convolutional Neural Networks (CNNs) and Fully Convolutional Net- works (FCNs), have shown promise but often struggle to integrate both global context and fine-grained details required for tasks like object recognition and navigation. To address these challenges, we present a Multi-Path Graph Attention Network with Reinforcement Learning (MPGA- RL-Net) adapted for robotic vision. This framework leverages a Multi- Path Feature Extraction (MPFE) module to capture multi-scale features at low, medium, and high resolutions, fusing them using an adaptive attention mechanism that assigns task-specific weights to each resolu- tion level. Super pixel-based segmentation is then applied to the fused feature map, representing regions as graph nodes, with Graph Convo- lutional Networks (GCNs) employed to model spatial relationships be- tween regions. Reinforcement Learning (RL) is further incorporated to dynamically adjust attention, allowing the model to focus on critical ar- eas, such as target objects or pathways, in real-time. Performance eval- uations demonstrate that MPGA-RL-Net enhances accuracy in robotic vision tasks, particularly in cluttered or dynamically changing environ- ments, achieving higher precision in object detection and adaptive focus on critical regions compared to conventional methods