Robotic Packaging Optimization with Reinforcement Learning and Real-World Data

Abstract

Intelligent manufacturing has become increasingly important in the food packaging industry due to the growing demand for enhanced productivity and flexibility while minimizing waste and lead times. This work explores the integration of such manufacturing in automated secondary robotic food packaging solutions that transfer food products into containers using pick-and-place robots. A major problem in these solutions is varying product supply caused by prior machinery. As a result productivity drops drastically when conveyor belt speeds are not optimally controlled. Conventional heuristic-based engineered approaches are used to address this issue but are inadequate, leading to noncompliance with industry's requirements. Reinforcement learning, on the other hand, has the potential of solving this problem by learning quick and predictive decision-making behavior based on experience. However, the lack of research in reinforcement learning for complex industrial robotic problems, limits its adoption in industry. Therefore, this work aims to investigate the feasibility of reinforcement learning in the robotic packaging industry. We propose a reinforcement learning framework, with policy inference in a highly complex control scheme, designed to optimize the conveyor belt speed of the secondary robotic packaging solution using real-world product supply data. The framework exceeds the 99.8 percent performance requirement and maintains quality at the required 100 percent when tested on real-world data. Compared to the current heuristic-based solution, our proposed framework improves productivity, has smoother control and reduces code execution time.