A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications

• URL: http://arxiv.org/abs/2306.01440v1
• Date: Fri, 2 Jun 2023 11:00:46 GMT
• Title: A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications
• Abstract summary: We present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.
• Score: 1.5136939451642133
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and batch size one production. Specifically, we present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. Each simulation run involves placing a specific number of goods of random color at the entry point. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. Following the installation of rivets, blue products must be transported to the exit, while green products are to be transported to storage. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.

Related papers

• GR2 Technical Report [58.300302585605095]
  GR2 is an end-to-end framework that combines mid-training on semantic IDs with reasoning-trace distilled from a stronger teacher.<n> GR2 delivers +18.7% R@1, +7.1% R@3, and +9.6% N@3 over legacy baselines on industrial-scale traffic.
  arXiv  Detail & Related papers  (2026-06-30T17:22:22Z)
• A Factory-Floor Deployment Case Study of VLA Pipelines for Industrial Packaging Task: Workflow, Failures, and Lessons [2.4341902779357536]
  Vision-Language-Action (VLA) policies have shown promising manipulation capabilities, yet their practical impact is often limited by the reliability demands of real-world deployment.<n>We present a deployment study of an industrial packaging task at Siemens Factory (GWE, Erlangen, Germany), where a robot must pick a transparent accessory bag from a cluttered pile, insert it into the remaining cavity of a cardboard package, and ensure that the bag and its contents remain below the closing plane.<n>Our goal is to understand the practical effort required to adapt a pretrained Pi0.5 policy to a single factory-floor task through iterative fine-tuning and
  arXiv  Detail & Related papers  (2026-05-25T20:46:22Z)
• FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios [58.34124792457706]
  The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution.<n>Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets.<n>We first construct a high-quality dataset that combines realworld 2D images and 3D point clouds, annotated with fine-grained domain semantics.<n>We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely verification, structural surface inspection, and assembly verification, revealing significant performance gaps.
  arXiv  Detail & Related papers  (2026-04-08T12:23:27Z)
• OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System [61.12400636463362]
  OnePiece is a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models.<n>OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics.
  arXiv  Detail & Related papers  (2025-09-22T17:59:07Z)
• Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics [0.0]
  This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. A concept for a model is proposed that provides a higher level of information detail to enhance scheduling accuracy and efficiency. The model extends traditional approaches to JSSPs by including job volumes, buffer management, transportation times, and machine setup times.
  arXiv  Detail & Related papers  (2024-09-18T09:12:40Z)
• Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework? [0.5772546394254112]
  We present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based system that allows for optional rework steps. We apply methods from causal machine learning, in particular double/debiased machine learning (DML) techniques, to estimate conditional treatment effects from data.
  arXiv  Detail & Related papers  (2024-06-17T08:14:40Z)
• Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling [2.2530496464901106]
  This work explores the effects of varying batch sizes on Reinforcement Learning solutions. It provides the practitioner with the ability to make an informed decision regarding the selection of an appropriate batch size. The findings of this work offer the potential for application in several industrial use cases with comparable scheduling problems.
  arXiv  Detail & Related papers  (2024-06-04T13:16:08Z)
• PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation [65.268245109828]
  We introduce PRILoRA, which linearly allocates a different rank for each layer, in an increasing manner, and performs pruning throughout the training process. We validate the effectiveness of PRILoRA through extensive experiments on eight GLUE benchmarks, setting a new state of the art.
  arXiv  Detail & Related papers  (2024-01-20T20:25:17Z)
• Causally Learning an Optimal Rework Policy [0.0]
  Reworking a production lot involves repeating a previous production stage with adjustments to ensure that the final product meets the required specifications. We apply machine learning to estimate the conditional treatment effect of a rework step during the color conversion process in opto-electronic semiconductor manufacturing.
  arXiv  Detail & Related papers  (2023-06-07T07:58:58Z)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization [0.0]
  This study proposes a model to find out about the adjustment of material inventory through transportation. The objective of this model is to minimize the whole production cost and total transportation cost.
  arXiv  Detail & Related papers  (2022-06-06T04:39:34Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• Supervised learning of sheared distributions using linearized optimal transport [64.53761005509386]
  In this paper we study supervised learning tasks on the space of probability measures. We approach this problem by embedding the space of probability measures into $L2$ spaces using the optimal transport framework. Regular machine learning techniques are used to achieve linear separability.
  arXiv  Detail & Related papers  (2022-01-25T19:19:59Z)
• Towards Standardizing Reinforcement Learning Approaches for Stochastic Production Scheduling [77.34726150561087]
  reinforcement learning can be used to solve scheduling problems. Existing studies rely on (sometimes) complex simulations for which the code is unavailable. There is a vast array of RL designs to choose from. standardization of model descriptions - both production setup and RL design - and validation scheme are a prerequisite.
  arXiv  Detail & Related papers  (2021-04-16T16:07:10Z)
• Detection and Classification of Industrial Signal Lights for Factory Floors [63.48764893706088]
  The goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor. Using methods commonly employed for traffic light recognition in autonomous cars, a system with an accuracy of over 99% in the specified conditions is presented.
  arXiv  Detail & Related papers  (2020-04-23T14:26:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.