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1. A Small-scale Implementation of Industry 4.0

   Aqlan, Faisal; Alabsi, Mohammed; Baxter, Ethan; Sreekanth, Ramakrishnan (January 2020, Proceedings of the 2020 IISE Annual Conference)

   null (Ed.)

   This paper discusses the implementation of Industry 4.0 in an educational setting. Simulation, virtual reality, analytics, robotics and automation, and 3D printing are integrated to develop a small-scale production line for producing and inspecting 3D printed parts. The system consists of a robot and controller, programmable logic controller, 3D printer, machine vision system, conveyor belt, 3-phase motor and motor controller, webcam, PC and monitor, Raspberry Pi computer, pneumatic system, beam sensor, simulation software, and VR equipment. The system components are connected via ethernet cables running to a basic ethernet switch. An ethernet router is also connected to the switch to resolve IP connection attempts by the connected components. A mini CNC machine is used to drill holes on small metal parts that are assembled with 3D printed parts and plastic bricks to make a car toy. A robot is pre-programmed to perform the assembly of the car toy and a Cognex® camera is used to inspect the parts. Deep learning models are used to predict the remaining useful life of the drilling bits. 

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2. Measuring Presence and Performance in a Virtual Reality Police Use of Force Training Simulation Prototype

   Garcia, Edward T; Ware, Stephen G; Baker, Lewis J (April 2019, Proceedings of the 32nd AAAI International Conference of the Florida Artificial Intelligence Research Society)

   In this paper we describe a virtual reality training simulation designed to help police officers learn use of force policies. Our goal is to test a training simulation prototype by measuring improvements to presence and performance. If successful, this can lead to creating a full-scale virtual reality narrative training simulation. The simulation uses a planner-based experience manager to determine the actions of agents other than the participant. Participants’ actions were logged, physiological data was recorded, and the participants filled out questionnaires. Player knowledge attributes were authored to measure participants’ understanding of teaching materials. We demonstrate that when participants interact with the simulation using virtual reality they experience greater presence than when using traditional screen and keyboard controls. We also demonstrate that participants’ performance improves over repeated sessions. 

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3. GSAC: Leveraging Gaussian Splatting for Photorealistic Avatar Creation with Unity Integration

   Zhang, Rendong; Watkins, Alexandra; Sarkar, Nilanjan (July 2025, IEEE; 2025 the 11th International Conference on Virtual Reality)

   Photorealistic avatars have become essential for immersive applications in virtual reality (VR) and augmented reality (AR), enabling lifelike interactions in areas such as training simulations, telemedicine, and virtual collaboration. These avatars bridge the gap between the physical and digital worlds, improving the user experience through realistic human representation. However, existing avatar creation techniques face significant challenges, including high costs, long creation times, and limited utility in virtual applications. Manual methods, such as MetaHuman, require extensive time and expertise, while automatic approaches, such as NeRF-based pipelines often lack efficiency, detailed facial expression fidelity, and are unable to be rendered at a speed sufficent for real-time applications. By involving several cutting-edge modern techniques, we introduce an end-to-end 3D Gaussian Splatting (3DGS) avatar creation pipeline that leverages monocular video input to create a scalable and efficient photorealistic avatar directly compatible with the Unity game engine. Our pipeline incorporates a novel Gaussian splatting technique with customized preprocessing that enables the user of ”in the wild” monocular video capture, detailed facial expression reconstruction and embedding within a fully rigged avatar model. Additionally, we present a Unity-integrated Gaussian Splatting Avatar Editor, offering a user-friendly environment for VR/AR application development. Experimental results validate the effectiveness of our preprocessing pipeline in standardizing custom data for 3DGS training and demonstrate the versatility of Gaussian avatars in Unity, highlighting the scalability and practicality of our approach. 

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4. Collaborative Virtual Assembly Environment for Product Design

   https://doi.org/10.1109/CSCI49370.2019.00114  

   Sharma, Sharad; Bodempudi, Sri-Teja; Arrolla, Manik; Upadhyay, Amit (December 2019, 2019 International Conference on Computational Science and Computational Intelligence (CSCI))

   Collaborative virtual assembly environment is a vital computer-aided design tool in product design and can be used as a learning and training tool. It helps in supporting complex product design by enabling designers to collaborate and communicate with other designers involved in the product design. This paper proposes a collaborative virtual assembly environment built in two phases for the immersive and non-immersive environments. Phase one was developed in Unity 3D using Virtual Reality Toolkit (VRTK) and Steam VR. Whereas, phase two was built using Vizard and Vizible. This work aims to allow scientists and engineers to discuss the concept design in a real-time VR environment so that they can interact with the objects and review their work before it is deployed. This paper proposes the system architecture and describes the design and implementation of a collaborative virtual assembly environment. The outcome of this work is to be able to resolve communication and interaction problems that arise during the concept-design phase. 

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5. Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

   https://doi.org/10.1115/1.4046293  

   López, Christian E.; Cunningham, James; Ashour, Omar; Tucker, Conrad S. (October 2020, Journal of Computing and Information Science in Engineering)

   Abstract This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design. 

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