Robotic Packaging Optimization with Reinforcement Learning

TL;DR

This paper presents a reinforcement learning framework for optimizing robotic food packaging, improving productivity and control smoothness while maintaining high packing quality in real-world scenarios.

Contribution

It introduces a novel RL-based approach to optimize conveyor speed in robotic packaging, outperforming conventional methods in efficiency and responsiveness.

Findings

Achieves 99.8% packed products in real-world tests

Maintains 100% filled boxes quality

Reduces computation time compared to existing solutions

Abstract

Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work investigates automated secondary robotic food packaging solutions that transfer food products from the conveyor belt into containers. A major problem in these solutions is varying product supply which can cause drastic productivity drops. Conventional rule-based approaches, used to address this issue, are often inadequate, leading to violation of the industry's requirements. Reinforcement learning, on the other hand, has the potential of solving this problem by learning responsive and predictive policy, based on experience. However, it is challenging to utilize it in highly complex control schemes. In this paper, we propose a reinforcement learning framework, designed to optimize the conveyor belt speed while minimizing interference with the rest of the control system. When tested on real-world data, the framework exceeds the performance requirements (99.8% packed products) and maintains quality (100% filled boxes). Compared to the existing solution, our proposed framework improves productivity, has smoother control, and reduces computation time.