Robotics & AI Research Lab

Covers the fundamental mechanics, sensing, and programming of robotic systems with the use of Arduinos and Lego Mindstorms.

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The course covers the most common types of robots and their application in different fields, along with the installation, configuration, and basic use of the ROS2 application framework.

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A project-based course where students apply their robotics skills to solve real-world challenges. The project can be done with both simulated and physical hardware.

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Focused on automated manufacturing, industrial arms, and systems integration for Industry 4.0.

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• Muntuumo, J., et al. (2026). ROS-Based Robotics Education in K–12: A Systematic Review of Teaching Approaches and Learning Outcomes. In 6th International Conference on Computing and Communication Networks (ICCCNet-2026). Accepted for publication.
• Mashood, H., et al. (2026). From Novice to Practitioner in Two Weeks: A Qualitative Study of Intensive Industrial Robotics Education. In Proceedings of 17th International Conference on Robotics in Education (RiE 2026). Accepted for publication.
• Pramanic, M. S., et al. (2026). RoboEdu: A Remote Robotics Education Environment for Learning ROS 2. In Proceedings of 17th International Conference on Robotics in Education (RiE 2026). Accepted for publication.
• Conde-González, M., et al. (2026). A Systematic Literature Review on the Benefits of Robotics and Active Learning Methodologies for Promoting STEAM Education among Students with Intellectual and Developmental Disabilities. International Journal of Engineering Education.
• Jormanainen, I., et al. (2025). Lessons Learnt from the Development of Remotely Accessible ROS 2 Learning Environment. RiE 2025, Springer.
• Jormanainen, I., et al. (2025). Simulation vs. Reality: Does It Matter? Investigating Student Motivation in Robotics Course Projects in Hybrid Learning Setting. RiE 2025, Springer.
• Jormanainen, I., et al. (2024). Intensive Robotics Course Projects Improve Higher Education Students’ Intrinsic Motivation and Learning Outcomes. RiE 2024, Springer.
• Conde, M.Á., et al. (2024). ROBOSTEAMSEN Project – Training SEN Teachers to Use Robotics for Fostering STEAM and Develop Computational Thinking. SIIE 2024, IEEE.

• Shipepe, A., et al. (2023). Integrating secondary school and primary school learners to grasp robotics in Namibia through collaborative learning. RiE 2023, Springer.
• Shipepe, A., et al. (2022). Co-Learning Computational and Design Thinking Using Educational Robotics: A Case of Primary School Learners in Namibia. Sensors.
• Pedersen, B.K.M.K., et al. (2022). Taxonomy for Educational Robotics at Schools. RiE 2022, Springer.
• Conde, M. Á., et al. (2021). RoboSTEAM Project: Integrating STEAM and Computational Thinking Development by Using Robotics and Physical Devices. IGI Global.
• Mrong, S., et al. (2021). Visualization tool for teaching and learning Artificial Neural Networks. TEEM 2021, ACM.
• Conde, M., et al. (2021). RoboSTEAM project the pilot phases. TEEM 2021, ACM.
• Conde, M. Á, et al. (2020). Adaptation of RoboSTEAM Project to the Pandemic Situation. TEEM 2020, ACM.
• Shipepe, A, et al. (2020). Educational Robotics Initiatives in Namibia and Worldwide. TEEM 2020, ACM.
• Jormanainen, I., & Tukiainen, M. (2020). Attractive Educational Robotics Motivates Younger Students to Learn Programming and Computational Thinking. TEEM 2020, ACM.
• Conde, M. Á, et al. (2020). Exchanging Challenge Based Learning Experiences in the Context of RoboSTEAM Erasmus+ Project. HCII 2020, Springer.
• Shipepe, A., et al. (2020). Screams of joy yield creative projects at the educational robotics workshop in Namibia. ICALT 2020, IEEE.

• Toivonen, T., et al. (2019). Augmented Intelligence in Educational Data Mining. Smart Learning Environments.
• Conde, M. Á, et al. (2019). RoboSTEAM – Definition of a Challenge Based Learning Approach for integrating STEAM and develop Computational Thinking. TEEM 2019, ACM.
• Jormanainen, I. (2018). On computer science major students’ motivation in a practically oriented robotics course. Koli Calling 2018, ACM.
• Suero Montero, C., & Jormanainen, I. (2017). Theater Meets Robot – Toward Inclusive STEAM Education. Edurobotics 2016, Springer.
• Toivonen, T., et al. (2017). An open robotics environment motivates students to learn the key concepts of artificial neural networks and reinforcement learning. RiE 2017, Springer.
• Laamanen, M., et al. (2015). Theater Robotics for Human Technology Education. Koli Calling 2015, ACM.
• Jormanainen, I., & Sutinen, E. (2014). Role blending in a learning environment supports facilitation in a robotics class. Journal of Educational Technology & Society.
• Jormanainen, I., et al. (2012). A novel learning environment to support teacher’s work in a robotics class. AROB 2012.
• Jormanainen, I., & Sutinen, E. (2012). Using Data Mining to Support Teacher’s Intervention in a Robotics Class. DIGITEL 2012, IEEE.

• Jormanainen, I., et al. (2009). Understanding open learning processes in a robotics class. Koli Calling 2009.
• Jormanainen, I., & Harfield, A. (2008). Supporting the teacher in educational robotics classes: work in progress. ICCE 2008.
• Zhang, Y., et al. (2008). An Implementation of Agency Architecture in Educational Robotics. ICALT 2008, IEEE.
• Jormanainen, I., et al. (2007). Pedagogical Agents for Teacher Intervention in Educational Robotics Classes: Implementation Issues. DIGITEL 2007, IEEE.
• Jormanainen, I., et al. (2006). Implementation of Intelligent Agents with Mobility in Educational Robotics Settings. WMUTE 2006, IEEE.
• Jormanainen, I., et al. (2006). Agency Architecture for Teacher Intervention in Robotics Classes. ICALT 2006, IEEE.

Our doctoral researchers focus on high-impact areas.

• Annastasia Shipepe, 2023: A Design Study on Educational Robotics in Namibia, Supervisors: Dr. Ilkka Jormanainen, Prof. Markku Tukiainen, UEF; Prof. Erkki Sutinen, University of Turku, UEF eREPO
• Tapani Toivonen, 2020: Open Machine Intelligence in Education, Supervisors: Dr. Ilkka Jormanainen, Prof. Markku Tukiainen, UEF, UEF eREPO
• Ilkka Jormanainen, 2014: Supporting Teachers in Unpredictable Robotics Learning Environments, Supervisors: Prof. Erkki Sutinen, UEF; Dr. Meurig Beynon, Emeritus Reader, University of Warwick; Prof. Kinshuk, Athabasca University, UEF eREPO

Master’s students focus on specialized applications in various fields within robotics.

• Haris Mashood, 12/2025: Evaluating Student Learning Outcomes in an Industrial Robotics Summer School – A Case Study, UEF eREPO
• Fabiano Junior Maia Manchein, 9/2024: Evaluating State-of-the-Art LiDAR SLAM in Challenging Indoor Environments with Glass and Specular Surfaces, UEF eREPO
• Teemu Voutilainen, 7/2024: Vahvistusoppimismallin siirtäminen Webots-simulaatiosta robotille (Reinforcement learning model from Webots to a robot), UEF eREPO
• Edward Brown, 9/2023: Comparison of sumo robot agents trained in real, sim-to-real and hybrid environments, UEF
• Patrik Sunnaborg, 5/2023: TinyML osana älykästä esineiden internetiä (TinyML as a part of the smart Internet of Things), UEF eREPO
• Natnael Yacob Asgedom, 10/2021: Robotic and block-based programming with Text Interpreter for motivating novice programmers, UEF eREPO
• Salseng Mrong, 3/2021: Visualization of the Artificial Neural Network for Machine Learning novices, UEF eREPO
• Tapani Toivonen, 6/2016: Extending Empirical Modelling with a physical computing framework, UEF eREPO
• Mikko Laamanen, 3/2015: Architecture of theatre robotics, UEF eREPO

Undergraduate projects often focus on hands-on applications and introductory robotics challenges.

• Glory Olusola: Model Context Protocol for Tool Using LLM Agents in Robotics Systems
• Lannie Uwu-khaeb: Use of Interbotix PincherX 100 robot arm
• Maxim Kasatkin: Tekoälyn hyödyntäminen robotin navigoinnissa ja autonomiassa (Utilizing artificial intelligence in robot navigation and autonomy)
• Linnea Tiihonen: Tekoäly ja robotiikka itseohjautuvissa ajoneuvoissa (Artificial intelligence and robotics in self-driving vehicles)
• Elmeri Saurus: Droonien käyttö tulevaisuuden taistelukentällä tekoälyn ja ELSO:n kehitysnäkökulmista (The Use of Drones on the Future Battlefield from the Development Perspectives of AI and Electronic Warfare)

RoboEdu is a server-based system that allows students to run Ubuntu and ROS (Robot Operating System) remotely. This setup enables practice with advanced robotics software on any computer without needing expensive, high-powered hardware.

The platform is hosted on a high-performance virtual machine at the Finnish CSC – IT Center for Science. It utilizes Docker containers to provide each user with a unique IP port and streaming interface. The environment supports intensive simulation tasks using hardware such as Nvidia Tesla P100 GPUs.

Technical Reference

Jormanainen, I., Pramanic, M. S., Kortelainen, M., & Vartiainen, P. (2025). Lessons Learnt from the Development of Remotely Accessible ROS 2 Learning Environment. Robotics in Education (RiE 2025). Springer.

Learning materials developed in collaboration with Henki Robotics, focusing on the fundamentals of the ROS 2 framework.

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Practical examples and software configurations for the UR3 collaborative robot, originally developed by Mohammadhossein Salari for our intensive summer school sessions.

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Our lab is equipped with a wide range of industrial and research-grade robotics hardware, supporting both the School of Computing and Department of Technical Physics initiatives.

Collaborative Manipulators

• UR3e: A highly flexible, collaborative robot arm for precise automation.
• PincherX 100: An educational research manipulator for vision-based control.

Mobile Robotics Platforms

• TurtleBot 4: The latest generation of open-source research platforms for ROS 2.
• TurtleBot 3 Waffle Pi: A versatile platform for mapping and navigation.
• UGV Rover: A high-performance off-road mobile platform for autonomous exploration.

Educational Kits & Sensors

• Arduino Kits: Over a dozen kits for fundamental electronics and basic control.
• Lego Mindstorms NXT: A dozen kits used for introductory robotics and STEM outreach.