Reflections on Practical Paths of Empowering Embodied AI Development via Computer Vision in Robotic Systems
TL;DR
This paper identifies several common challenges encountered during industry deployment, including insufficient stability in recognizing complex scenes, difficulty balancing computational power with recognition accuracy, significant gaps between simulation training and real-world conditions, and a lack of unified application standards across industries.
Abstract
Currently, most commercial robots rely on fixed programs to perform repetitive tasks. The visual modules equipped on these devices have limited anti-interference capabilities, making it difficult to adapt to complex structures and variable environments, which in turn limits the practical effectiveness of robotic intelligence deployment. Drawing from hands-on experience in robot debugging and project implementation, this paper explores the integration of computer vision and embodied intelligence. Based on common application scenarios such as industrial sorting, power inspection, and intelligent services, it analyzes specific methods by which visual technologies assist robots in environmental perception, intelligent decision-making, motion adjustment, and model updating. The study identifies several common challenges encountered during industry deployment, including insufficient stability in recognizing complex scenes, difficulty balancing computational power with recognition accuracy, significant gaps between simulation training and real-world conditions, and a lack of unified application standards across industries. Addressing these practical issues, the paper proposes feasible improvement strategies from four perspectives: algorithm enhancement, computational power upgrade, simulation optimization, and the establishment of industry standards. The conclusions drawn from this research offer practical insights for advancing robot vision technology, expanding application scenarios, and promoting standardized development within the industry.