Software-defined self-learning control system for industrial robots by using reinforcement learning.

ABSTRACT: Modern manufacturing requires systems that respond quickly and flexibly to changing production demands. Although software-based control systems that update programs to perform new tasks on existing equipment can partially address these requirements, they have limitations when applied to manufacturing equipment. Moreover, they face challenges in providing immediate responses to unexpected conditions and lack skilled human operators. This paper proposes a self-learning control system that integrates anomaly detection and reinforcement learning, enabling existing equipment to adapt to new tasks and equipment states through software updates. The proposed system uses a virtual environment that imitates various anomalous statuses in a self-learning approach. In this virtual environment, reinforc

SUBMITTER: Moon J 

PROVIDER: S-EPMC12804974 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature