Robotic Packaging Optimization with Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 26 Mar 2023.
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References · 12
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
- 2020On Layer Normalization in the Transformer Architecturecited by 906
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
- 2018Safe Exploration in Continuous Action Spacescited by 237
- 2019Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learningcited by 90
- 2020Reinforcement Learning with Random Delayscited by 56
- 2019Risk Averse Robust Adversarial Reinforcement Learningcited by 54
- 2021How Do Adam and Training Strategies Help BNNs Optimization?cited by 46
- 2019Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flightcited by 25
- 2018Reinforcement Learning Testbed for Power-Consumption Optimizationcited by 18
- 2021Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Roboticscited by 18
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