LATIDIA · Robótica

Motor de percepción visual: inferencia multicabezal rápida y flexible para tareas de visión robótica

arXiv: 2508.11584v3Tipo de anuncio: reemplazar Resumen: Implementar múltiples modelos de aprendizaje automático en plataformas robóticas con recursos limitados para diferentes tareas de percepción a menudo resulta en cálculos redundantes, gran memoria

La noticia

arXiv:2508.11584v3 Announce Type: replace Abstract: Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant computations, large memory footprints, and complex integration challenges. In response, this work presents Visual Perception Engine (VPEngine), a modular framework designed to enable efficient GPU usage for visual multitasking while maintaining extensibility and developer accessibility. Our framework architecture leverages a shared foundation model backbone that extracts image representations, which are efficiently shared, without any unnecessary GPU-CPU memory transfers, across multiple specialized task-specific model heads running in parallel. This design eliminates the computational redundancy inherent in feature extraction component when deploying traditional sequential models while enabling dynamic task prioritization