Robotics firm AgiBot has announced the deployment of its Real-World Reinforcement Learning (RW-RL) system on a pilot production line operated by Longcheer Technology. According to the company, this marks the first use of reinforcement learning in an industrial robotics environment.
The project integrates reinforcement learning—a machine learning technique in which robots learn by trial and feedback—into live manufacturing systems. AgiBot said the deployment bridges developments in embodied artificial intelligence with industrial automation, enabling robots to learn and adapt to production conditions in real time.
AgiBot’s RW-RL system was designed to address challenges in precision manufacturing, where conventional automation often requires complex setup and limited flexibility. The company stated that its robots can acquire new skills within minutes, operate stably over extended periods, and adjust to changes in production with minimal reconfiguration.
The system reportedly reduces the time required for training new tasks from weeks to minutes and allows retraining for new products without custom fixtures or tooling. AgiBot said this approach enhances adaptability across different production lines and environments, enabling reuse of robot configurations.
The company described the deployment as the result of several years of reinforcement learning research. Chief Scientist Dr. Jianlan Luo and his team previously demonstrated that reinforcement learning could achieve stable performance on physical robots, forming the basis for AgiBot’s industrial implementation.
The pilot with Longcheer Technology was conducted under near-production conditions to evaluate the system’s stability and performance. Both companies plan to expand the use of real-world reinforcement learning to other precision manufacturing applications, including consumer electronics and automotive components.
AgiBot said future development will focus on modular, rapidly deployable robotic systems that can integrate with existing production infrastructure.
