Maria Pia Fanti (IEEE Fellow and Fellow of the Asia-Pacific AIA) received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1983. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983, she has been with the Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy, where she is currently a Full Professor of system and control engineering and Chair of the Laboratory of Automation and Control. Her research interests include modeling and control of complex systems, intelligent transportation systems, smart logistics; Petri nets; consensus protocols; fault detection. Prof. Fanti has published more than +310 papers and two textbooks on her research topics. She was senior editor of the IEEE Trans. on Automation Science and Engineering and member at large of the Board of Governors of the IEEE Systems, Man, and Cybernetics Society. Currently, she is Associate Editor of the IEEE Trans. on Systems, Man, and Cybernetics: Systems, member of the AdCom of the IEEE Robotics and Automaton Society, and chair of the Technical Committee on Automation in Logistics of the IEEE Robotics and Automation Society. Prof. Fanti was General Chair of the 2011 IEEE Conference on Automation Science and Engineering, the 2017 IEEE International Conference on Service Operations and Logistics, and Informatics and the 2019 Systems, Man, and Cybernetics Conference.

Shaoping Bai is a full professor at the Department of Materials and Production, Aalborg University (AAU), Denmark. His research interests include wearable sensors, medical and assistive robots, and exoskeletons. Prof. Bai leads several national and international research projects in exoskeletons, including EU AXO-SUIT and IFD Grand Solutions project EXO-AIDER, and Danish Independent Research Council project VIEXO, among others. He is a recipient of several best paper awards in conferences including IEEE CIS-RAM 2017, IFToMM MEDER 2018, MESROB 2023, IFToMM ISRM 2026, in addition to   WearRAcon2018 Grand Prize of Innovation Challenges and the Best Paper Award 2024 from Biomimetic Intelligence and Robotics journal.  Prof. Bai is an associate editor of Robotica,  ASME Letters in Translations Robotics, and Wearable Technologies. He is the founder of BioX ApS and an elected member of IFToMM Executive Council.

Speech Title: Wearable Exoskeletons: From Kinematic Compatibility to Physiological and Cognitive Compatibility
Abstract: Wearable exoskeleton technologies are advancing rapidly and finding widespread applications in healthcare, industry, and assistive services. This keynote presents emerging trends and key challenges in exoskeleton research, with a focus on kinematic design, human biomechanics, sensing, and control for effective human-robot interaction.
The talk highlights the design, development, and innovations achieved at the Exoskeleton Laboratory, Aalborg University, Denmark. Fundamental research challenges in biomechanical modeling, linkage and mechanism design, and experimental validation will be discussed. Design examples will include novel exoskeleton joint mechanisms, upper- and lower-limb exoskeletons, and hand exoskeletons, encompassing both active and passive systems.
Beyond traditional kinematic compatibility, the presentation explores the transition toward physiological and cognitive compatibility, aiming to create exoskeletons that better understand, support, and adapt to human intentions and capabilities. In this context, forcemyography (FMG)-based sensing and control approaches for enhanced human-robot interaction will be introduced, demonstrating how physiological signals can improve intuitiveness, comfort, and user acceptance.

Jouni Mattila is a Full Professor of Machine Automation at Tampere University, Finland, a position he has held since 2011. He received the M.Sc. and Doctor of Technology degrees from Tampere University of Technology in 1995 and 2000, respectively. From 1998 to 2000, he was a visiting scientist at the Robotics and Control Laboratory, University of British Columbia, Canada.
His research focuses on modeling, estimation, and control of heavy-duty robotic systems, with particular emphasis on autonomous mobile manipulators, flexible manipulators, teleoperation, and electrification of heavy-duty machinery. His work spans nonlinear and model-based control, learning-based methods, distributed sensing and state estimation, and human–robot interaction, with applications in mining, construction, forestry, material handling, and fusion remote handling. A central theme is bringing advanced robotics, learning, and safety-aware control methods from theory to full-scale machines operating autonomously in demanding real-world environments.
Prof. Mattila has initiated and led numerous national and European research projects in close collaboration with industry. He has supervised 24 completed doctoral degrees since 2011 and coordinated major European doctoral training programmes in robotics and remote handling. He served on the Technical Editorial Board of the IEEE/ASME Transactions on Mechatronics from 2015 to 2020 and as Publicity Chair of the 2025 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

Speech Title: From Mechanism to Motion: RoboCompiler for Consistent Control and Simulation
Abstract: A robot may have one physical mechanism, yet its controller, dynamics library, and simulator often rely on different computational models. For robots with closed kinematic loops, coupled actuators, or changing supports, keeping these models mechanically consistent is a central challenge. This talk presents RoboCompiler, a framework that compiles a physical mechanism graph into shared interfaces for robot assembly, motion, actuation, and dynamics. I will explain how closure constraints, local motion mappings, and actuator interfaces that preserve mechanical power are generated together, and why constraint curvature matters for acceleration and force consistency. Simulation studies spanning an excavator, legged robots, a robotic manipulator, and a Stewart platform illustrate model reuse across MuJoCo, Pinocchio, and Isaac Sim, alongside independent dynamics checks and computational savings. The talk will discuss the framework's assumptions and demonstrate how a shared physical description can connect robot structure to reusable models for control and simulation.

Prof. Dr. Annette Stahl is a Professor of Robotic Vision and Head of the Robotic Vision Group at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Norway. She received her PhD in Mathematics and Computer Science from Heidelberg University, Germany, in 2009. Before joining NTNU as an Onsager Fellow in 2016, she held research positions at Dublin City University, NTNU, and SINTEF Ocean.
Her research focuses on robotic vision, resilient perception, and intelligent autonomous systems, with applications ranging from underwater and maritime robotics to industrial robots. Her work includes visual navigation, multi-modal sensor fusion, active perception, and 3D scene understanding. She is a key scientist at the Norwegian Centre for Embodied AI (NCEI) and a researcher at the Centre for Research-based Innovation SFI AutoShip, focusing on perception and situational awareness for autonomous systems. She was previously affiliated with the NTNU Centre of Excellence for Autonomous Marine Operations and Systems (AMOS).

Speech Title: Resilient Perception and Semantic Scene Understanding for Intelligent Autonomous Robots
Abstract: Robots operating in real-world environments must be able to perceive and understand their surroundings, even when sensing conditions are far from ideal. Changing illumination, poor visibility, dynamic environments, and unreliable sensor observations pose significant challenges for autonomous operation. Developing perception systems that can cope with these conditions and remain reliable over time is therefore an important research challenge.
In this talk, I will present research from our Robotic Vision Lab at NTNU on resilient perception for autonomous robotic systems. Using examples from underwater robotics, aquaculture, autonomous ships, and industrial settings, the talk addresses some of the challenges encountered when bringing robotic vision from controlled environments into real-world applications. This includes our work on robust visual SLAM and active perception, as well as research carried out within SFI AutoShip, where we develop robust perception methods for autonomous vessels. Particular attention is given to how combining information from different sensors can improve object tracking and situational awareness in challenging maritime environments.
Moving beyond robust perception, the talk introduces our recent perception-related research within the Norwegian Centre for Embodied AI (NCEI), focusing on semantic scene understanding as an important step towards embodied intelligence. For robots to interact intelligently with their environment, they need to go beyond detecting and localizing objects and develop a more structured understanding of the world around them. Our work on open-vocabulary 3D scene graphs explores how objects and their spatial and semantic relationships can be represented to support higher-level scene understanding and reasoning. This connects robotic perception with embodied AI and opens up new possibilities for robots to interpret and interact with their surroundings.

Masoumeh Iran Mansouri is an Associate Professor in the School of Computer Science at the University of Birmingham, UK. Her research spans several complementary areas, including robot planning, cultural robotics, and the social study of AI/robotics. She was previously a researcher at the Centre for Applied Autonomous Sensor Systems at Örebro University, Sweden, and has held visiting positions at the Oxford Robotics Institute and in Sven Koenig's lab at the University of Southern California. Her core technical work addresses automated planning for (semi-)autonomous robotic systems, both single- and multi-robot, that share space with humans. This includes hybrid methods combining task and motion planning, scheduling, and spatio-temporal reasoning for real-world settings from mining to museum service robotics as well as planning under uncertainty and failure recovery in assembly tasks. She has also worked extensively on settings that require coordination and collaboration among robots, developing methods for multi-robot trajectory optimization and formation planning, with applications such as object transport and fleet management in warehouses. Her research also extends to cultural robotics, a field dedicated to studying the effects of integrating cultural models into robots. She approaches cultural robotics from a critical perspective, calling for a deeper understanding of the new forms of culture that emerge in human–robot interactions, the impacts of these cultures on participants, the contexts in which such interactions occur, and their influence on broader societies.

Speech Title: Robust Decision Making in Multi-Agent Systems
Abstract: This talk explores several principles of robust decision-making in multi-agent systems, focusing on how autonomous (multi-)robots can operate reliably in complex, uncertain, and dynamic environments shared with humans. In particular, I will cover aspects such as problem decomposition, verification, failure handling, and responsible modelling in the context of real-world robotic applications, including mining, object transportation, automated assembly, museum settings, and construction sites.