Research Progress on the Integration of Robot Vision, Computer Vision and Machine Learning

What is it about?

In recent years, with the rapid development of artificial intelligence and computer vision technology, robot vision system has made remarkable progress. These systems improve the perception and decision-making capabilities of robots in complex environments by combining deep learning and machine vision technologies. Deep learning algorithms excel in image processing and feature extraction, enabling robots to more accurately identify and track target objects1-5. In addition, the progress of machine vision technology also provides strong support for the application of robots in agriculture, industry, medical and other fields6-10.

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Why is it important?

Robot vision is transitioning from “single-modal perception” to “multi-modal cognition” and its deep integration with CV/ ML has promoted paradigm changes in fields such as intelligent manufacturing and medical surgery37. In the future, we need to focus on three major directions: (1) collaborative optimization of lightweight models and edge computing; (2) Neural symbolic system enhances decision interpretability; (3) Construction of data ecology driven by federated learning. Academia and industry need to jointly build an open platform (OpenX-Embodiment) to accelerate the transformation of technology from laboratories to factories.