A Unified Intelligent Framework for Multimedia Robotics through the Integration of Computer Vision and Natural Language Processing Techniques
Keywords:
Multimedia Robotics, Computer Vision, Natural Language Processing, Multimodal LearningAbstract
The convergence of computer vision and natural language processing (NLP) has significantly advanced the capabilities of multimedia robotics systems, enabling more intuitive human–robot interaction and autonomous decision-making. This research proposes a unified intelligent framework that integrates visual perception and linguistic understanding into a cohesive architecture for multimedia robotics applications. The study synthesizes theoretical foundations from multimodal intelligence, deep learning, and semantic representation to design a scalable and adaptive system capable of interpreting visual data and generating context-aware linguistic responses. The proposed framework incorporates modules for perception, semantic fusion, knowledge representation, and action planning, leveraging advances in multimodal embedding and deep neural architectures. Experimental insights indicate that the integration of vision and language enhances interpretability, contextual awareness, and task efficiency in robotics systems. The findings highlight the effectiveness of unified architectures in bridging the semantic gap between visual data and linguistic expression, while also identifying limitations related to computational complexity and data dependency. This research contributes a structured, extensible model for multimedia robotics, with implications for intelligent automation, assistive robotics, and human-centered AI systems.
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Copyright (c) 2026 Dr. Majid Fallahi, Dr. Somayeh Khalili (Author)
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