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1. FORE: A Student-Centered Framework for Accessible Robotics Education through Simulation and Interactive Learning

   Jamali, H; Debolt, A; Dalton, H; Layosa, J; Macy, I; Shill, P; Feil-Seifer, D; Harris, F; Dascalu, S; Wu, R (June 2025, 2025 ASEE Conference)

   Robotics education is often constrained by the high cost and limited accessibility of physical robots, which can hinder the learning experience for many students. To address this challenge, the Fundamentals of Robotics Education (FORE) project, part of a larger NSF-funded collaborative work, was developed to create an accessible and comprehensive online learning platform. FORE provides a student-centered approach to robotics education, featuring a robust code editor, real-time simulation, and interactive lessons. This paper presents the architecture and implementation of the FORE platform, highlighting its key components, including the backend simulation using Gazebo and ROS2, a frontend visualizer built with Three.js, and the integration of a Python-based coding environment. We discuss the development process, the contributions of the student team, and the challenges encountered during the project. The results demonstrate the platform’s effectiveness in making robotics education more easily available. These findings originate from software testing and utilization by senior computer science students, as well as feedback from participants at the University of Nevada, Reno College of Engineering’s annual Capstone Course Innovation Day. The platform allows students to gain hands-on experience without the need for physical hardware. Its adaptability enables it to serve a broad audience of undergraduate students, offering an encompassing and accessible solution for modern robotics education. 

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2. Speech-Guided Sequential Planning for Autonomous Navigation Using Large Language Model Meta AI 3 (Llama3)

   https://doi.org/10.1007/978-981-96-3519-1_15  

   Srivastava, Alkesh K; Dames, Philip (March 2025, Springer Nature Singapore)

   In social robotics, a pivotal focus is enabling robots to engage with humans in a more natural and seamless manner. The emergence of advanced large language models (LLMs) has driven significant advancements in integrating natural language understanding capabilities into social robots. This paper presents a system for speech-guided sequential planning in pick and place tasks, which are found across a range of application areas. The proposed system uses Large Language Model Meta AI (Llama3) to interpret voice commands by extracting essential details through parsing and decoding the commands into sequential actions. These actions are sent to DRL-VO, a learning-based control policy built on the Robot Operating System (ROS) that allows a robot to autonomously navigate through social spaces with static infrastructure and crowds of people. We demonstrate the effectiveness of the system in simulation experiment using Turtlebot 2 in ROS1 and Turtlebot 3 in ROS2. We conduct hardware trials using a Clearpath Robotics Jackal UGV, highlighting its potential for real-world deployment in scenarios requiring flexible and interactive robotic behaviors. 

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3. Reflections of Undergraduate Engineering Students Completing a Cross-Disciplinary Robotics Project with Pre-Service Teachers and Fifth Graders in an Electromechanical Systems Course.

   Kaipa, K; Kidd, J; Kumi, I; Ringleb, S; Ayala, O; Gutierrez, K; Pazos, P; Cima, F; Rhemer, D (June 2024, ASEE Annual Conference & Exposition)

   Engineering is becoming increasingly cross-disciplinary, requiring students to develop skills in multiple engineering disciplines (e.g., mechanical engineering students having to learn the basics of electronics, instrumentation, and coding) and interprofessional skills to integrate perspectives from people outside their field. In the workplace, engineering teams are frequently multidisciplinary, and often, people from outside of engineering are part of the team that brings a product to market. Additionally, teams are often diverse in age, race, gender, and in other areas. Teams that creatively utilize the contrasting perspectives and ideas arising from these differences can positively affect team performance and generate solutions effective for a broader range of users. These trends suggest that engineering education can benefit from having engineering students work on team projects that involve a blend of cross-disciplinary and mixed-aged collaborations. An NSF-funded project set out to explore this idea by partnering undergraduate engineering students enrolled in a 300-level electromechanical systems course with preservice teachers enrolled in a 400-level educational technology course to plan and deliver robotics lessons to fifth graders at a local school. Working in small teams, students designed, built, and coded bio-inspired robots. The collaborative activities included: (1) training with Hummingbird Bit hardware (Birdbrain Technologies, Pittsburgh, PA) (e.g. sensors, servo motors) and coding platform, (2) preparing robotics lessons for fifth graders that explained the engineering design process, and (3) guiding the fifth graders in the design of their robots. Additionally, each engineering student designed a robot following the theme developed with their education student and fifth-grade partners. 

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4. Digital twin-enabled real-time control for robot arm-based manufacturing via reinforcement learning

   https://doi.org/10.1007/s10845-025-02728-9  

   Ali, Matsive; Giri, Sandesh; Yang, Qin; Liu, Sen (November 2025, Journal of Intelligent Manufacturing)

   Recent advancements in Digital Twin (DT) technology have opened new avenues for smart manufacturing. These systems increasingly depend on adaptive control mechanisms to optimize complex processes and reduce production wastage. This research presents an innovative approach that integrates Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm with DT technology with Robot Operating System 2 (ROS2) to enable real-time adaptive control in robotic manufacturing. Our experimental setup consists of a ViperX 300 S robot arm, in which two distinct Tasks: (1) static target reaching and (2) dynamic target following were implemented for simulating adaptive control of manufacturing process. The innovative system architecture combines Unity game engine’s simulation environment with ROS2 for seamless and robust DT synchronization. We implemented a hierarchical reward structure to address common RL challenges, including local minima avoidance, convergence acceleration, and training stability, while leveraging transfer learning to efficiently adapt trained behavior models across tasks. Experimental results demonstrate rapid policy convergence and robust task execution, with performance metrics including cumulative reward, value loss, policy loss, and entropy validating the effectiveness of the approach. To the best of our knowledge, this is the first study to integrate Unity with ROS2-based DT for real-time synchronization and adaptive physical robot control using RL. Unlike prior works limited to offline or low-frequency simulations, our framework achieves stable 20 ms joint-level synchronization, enabling deployment of learned behaviors directly to physical robotic systems through virtual platform. This work advances the integration of RL with realistic DT framework for industrial and manufacturing robotics applications, providing a framework for enhanced adaptive real-time control in smart additive manufacturing (AM) processes. 

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5. Reflections of Undergraduate Engineering Students Completing a Cross-Disciplinary Robotics Project with Preservice Teachers and Fifth Graders in an Electromechanical Systems Course

   https://doi.org/10.18260/1-2--47929  

   Kaipa, Krishnanand; Kidd, Jennifer; Kumi, Isaac; Ringleb, Stacie; Ayala, Orlando; Gutierrez, Kristie; Pazos, Pilar; Cima, Francisco; Rhemer, Danielle (June 2024, ASEE Conferences)

   Abstract. Engineering is becoming increasingly cross-disciplinary, requiring students to develop skills in multiple engineering disciplines (e.g., mechanical engineering students having to learn the basics of electronics, instrumentation, and coding) and interprofessional skills to integrate perspectives from people outside their field. In the workplace, engineering teams are frequently multidisciplinary, and often, people from outside of engineering are part of the team that brings a product to market. Additionally, teams are often diverse in age, race, gender, and in other areas. Teams that creatively utilize the contrasting perspectives and ideas arising from these differences can positively affect team performance and generate solutions effective for a broader range of users. These trends suggest that engineering education can benefit from having engineering students work on team projects that involve a blend of cross-disciplinary and mixed-aged collaborations. An NSF-funded project set out to explore this idea by partnering undergraduate engineering students enrolled in a 300-level electromechanical systems course with preservice teachers enrolled in a 400-level educational technology course to plan and deliver robotics lessons to fifth graders at a local school. Working in small teams, students designed, built, and coded bio-inspired robots. The collaborative activities included: (1) training with Hummingbird Bit hardware (Birdbrain Technologies, Pittsburgh, PA) (e.g. sensors, servo motors) and coding platform, (2) preparing robotics lessons for fifth graders that explained the engineering design process, and (3) guiding the fifth graders in the design of their robots. Additionally, each engineering student designed a robot following the theme developed with their education student and fifth-grade partners. This paper reports on the reflections of the engineering students after completing a cross-disciplinary robotics project with preservice teachers and fifth graders with the goals of (1) assessing the suitability of the project to the specific course, (2) analyzing the nature of the balance between course and project workload/objectives, (3) benefits and challenges of participating in the project, and (4) evaluating the overall effectiveness of the intervention. Student reflections collected at the conclusion of the semester from implementations in Spring 2022 and Spring 2023 were analyzed for this study. Findings from a thematic qualitative analysis of the reflections revealed benefits such as students’ perceived gains in coding skills, reinforcement of engineering concepts learned in class, acquisition of interprofessional skills (e.g., communication with technical and non-technical audiences, cross-disciplinary collaboration), and engineering-pedagogical skills such as lesson planning and classroom management. Students also reflected on opportunities to incorporate creative design insights when brainstorming with non-engineers. Students’ perceived challenges were mainly related to workload, time management, course organization, and teaching/interacting with the fifth graders. These findings provide insightful suggestions for future interventions in undergraduate engineering courses. 

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