Continual learning using GPT-4o and natural language processing: a robotic implementation for printed circuit board recycling

Purpose This study aims to develop a self-improving system that continuously improves performance with minimal human intervention. The system combines a CNN model, GPT-4o and user-assisted data set refinement via natural language to classify electronic components in a robotic printed circuit board (PCB) recycling scenario. Design/methodology/approach A CNN-based object detection model serves as the system’s core vision tool. When recognition confidence is low, the system engages GPT-4o and the user for classification through natural language input. The collected data are used to update the training set and re-train the model, enabling continuous performance improvement. A robotic manipulator evaluates the developed algorithm in real hardware implementation task. Findings Experimental results demonstrate that the proposed framework significantly improves classification accuracy over time. The integration of GPT-4o for interactive data refinement reduces manual labeling efforts while strengthening the system’s ability to identify and sort PCB recycling components accurately. Research limitations/implications Several challenges remain concerning the continual learning model and GPT-4o’s image recognition capabilities, which will be addressed to improve the system. Evaluations using Grad-Cam showed that even if the object’s features could not be grasped at first, it became possible to recognize it through continual learning. Therefore, it is expected that continual learning can improve accuracy for objects with poor object recognition accuracy. Practical implications Continuous incremental learning using speech recognition and GPT-4o was proposed in this paper. The incremental learning improved the recognition rates. The proposed algorithm is implemented to classify objects in the images for the recycling of circuit boards. The implementation demonstrated the practical value of continuous learning by enabling the robot to handle dynamic and diverse sorting tasks with greater efficiency and precision, reducing errors and improving overall operational performance. The proposed continual learning can be implemented in a wide range of applications. Originality/value The concept of continuous learning by using GPT-4o and natural language for data set updates is a novel contribution. In addition, the application of continual learning for robotic PCB manipulation is somewhat unique.