2025 7th International Conference on Control and Robot (ICCR 2025)

Dr. Subhas Mukhopadhyay, Professor FIEEE (USA), FIEE (UK), FIETE (India), Macquarie University, Australia

Biography: Subhas holds a B.E.E. (gold medalist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). He has over 36 years of teaching, industrial and research experience. Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network (WSN), Internet of Things (IoT), Robotics, Mechatronics and Drones etc. He has supervised over 60 postgraduate students and over 200 Honours students. He has examined over 80 postgraduate theses.
He has been co-inventor of 14 patents and published over 500 papers in different international journals and conference proceedings, written ten books and sixty two book chapters and edited twenty conference proceedings. He has also edited forty five books with Springer-Verlag and forty journal special issues. He has organized over 25 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 455 presentations including keynote, invited, tutorial and special lectures. As per Scholargoogle, his total citation is 26291 and h-index is 83.
He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India). He is a Topical Editor of IEEE Sensors journal, an associate editor of IEEE Transactions on Instrumentation and Measurements and IEEE Reviews in Biomedical Engineering (RBME). He is EiC of the International Journal on Smart Sensing and Intelligent Systems. He was a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2022. He chairs the IEEE Instrumentation and Measurement Society NSW chapter.

More details can be available at https://scholar.google.com.au/citations?user=8p-BvWIAAAAJ&hl=en;
https://orcid.org/0000-0002-8600-5907; http://web.science.mq.edu.au/directory/listing/person.htm?id=smukhopa.

Speech Title: Next Generation of Smart Devices, IoT, Robots and Drones

Abstract: The advancement of sensing technologies, embedded systems, wireless communication technologies, nano-materials, miniaturization, vision sensing and processing speed makes it possible to develop smart mechatronics and machine systems. This seminar will discuss recent research and developmental activities on different sensors and sensing system, Mechatronics, (robotics and drones), IoT along with machine visions at Macquarie University as applicable to medical science, biology and environmental monitoring.

Dr. Shinichi Hirai, Professor, Ritsumeikan University, Japan<

Biography:Shinichi Hirai received his Ph.D degree in applied mathematics and physics from Kyoto University in 1991. He joined the newly established Department of Robotics at Ritsumeikan University in 1996. Since 2002, he has been a Professor in the department. He was a Visiting Researcher with the Massachusetts Institute of Technology in 1989 and was an Assistant Professor with Osaka University from 1990 to 1996. His current research interests include soft robotic hands, soft sensors, soft object manipulation, and soft object modeling. He received the Robotics Society of Japan (RSJ) Best Paper Award in 2008, FOOMA Japan Academic Plaza Award in 2018, and Int. Conf. on Ubiquitous Robots Best Paper Award in 2020. He is a member of IEEE, RSJ, JSME, and SICE.

Speech Title: Soft-Material Robotic Hands for Grasping and Manipulation

Abstract: This talk introduces soft-material robotic hands for object grasping and manipulation. There remain many handling operations performed by humans in food industry, agriculture, and low-volume production. These operations require flexibility and adaptability against object variances and changeovers. Soft-material robotic hands will contribute to such operations. In this talk, I will introduce soft-material robotic hands designed and fabricated for handling of food, agricultural products, textiles, and living organisms.

Dr. Juntao Fei, Professor, Hohai University, China

Biography:Juntao Fei is working as a Professor at the College of Artificial Intelligence and Automation, Director of Institute of Electrical and Control Engineering, Hohai University. He received his M.S and Ph.D. degree from the University of Akron, USA. He was visiting scholars at University of Virginia, USA, North Carolina State University, USA respectively. He ever served as an assistant professor at the University of Louisiana, USA. His fields of interest include neural network, intelligence control, mchatronics and robotics, adaptive control. He is IAAM Fellow, Vebleo Fellow, IEEE Senior Member. He was a Principal Investigator of 30 projects in the last ten years. He has published over 300 papers in Journals and Conferences and five books, 220 SCI Index papers. He authorized 115 invention patents. He has actively served as associate editors for a number of International Journals; chairs for numerous International Conferences. He is an awardee of the Recruitment Program of Global Experts (China). He is selected as the top 0.05% scientists in the world, and "highly cited scholars in China" by Elsevier.

Speech Title: Fuzzy Neural Finite-time Sliding Mode Control of DC-DC Buck Converter

Abstract: This speech will discuss a nonsingular fast terminal sliding mode control (NFTSMC) with a self-organizing Chebyshev fuzzy neural network (SOCFNN) to achieve voltage tracking control of a DC-DC buck converter. The NFTSMC can ensure the finite-time convergence property of the tracking error. The SOCFNNis utilized to estimate the nonlinear dynamics, in which a novel structure learning mechanism is constructed to dynamically generate the number of the fuzzy rules. Both the simulation and experimental comparisons illustrate that the proposed controller presents higher voltage tracking accuracy and faster dynamic response.

Dr. Hyungpil Moon, Professor, Sungkyunkwan University, Korea

Biography:Professor Hyungpil Moon is a faculty member in the Department of Mechanical Engineering at Sungkyunkwan University and serves as the chairperson of the Department of Intelligent Robotics. His research interests include robotic manipulation, autonomous mobile robots, sensors and actuators, and the application of machine learning in robotics. Professor Moon received his Ph.D. from the University of Michigan, Ann Arbor and has experience as a postdoctoral researcher at the Robotics Institute of Carnegie Mellon University. He is an Associate Vice President of IEEE RAS, Senior Editor of IEEE RA-L and TASE.  His notable research achievements include the design and control of various robotic systems, particularly in the development of logistics and service robots. Recently, he has been focusing on AI applications in robotics, dual-arm robotic systems, and developing efficient package handling technologies for logistics environments.

Speech Title: From Laboratory Air Lines to Self-Contained Pneumatic Intelligence: Pressure Control for Untethered Soft Robots

Abstract: Traditional pneumatic soft robots have relied on laboratory compressed-air lines or tethered high-pressure pneumatic sources, confining their operation to bench-top environments. While such systems enabled foundational research in soft pneumatic actuation, their dependence on external air supplies has fundamentally limited the mobility, autonomy, and scalability of soft robots.
This keynote presents technology developments toward self-contained pneumatic intelligence, where air generation, pressure boosting, and control are fully integrated within the robot. At the core of this platform are two key innovations:
- a multi-channel pneumatic boosting system that achieves fast, independent, and stable regulation across multiple soft actuators, and
- a reinforcement learning-based bidirectional pressure control system capable of simultaneous positive and negative pressure generation.
Together, these systems replace large stationary compressors with compact on-board pneumatic power packs, enabling untethered soft robots with precise, high-bandwidth control. The ability to generate and regulate both positive and negative pressures with sufficient airflow rates allows the actuators to exert significantly higher forces and achieve bidirectional motion at high speed within lightweight and compliant bodies.
Building upon this platform, we developed a family of soft actuators—including both linear and rotary types—that leverage this bidirectional pneumatic mechanism to deliver enhanced torque, displacement, and mechanical robustness. Additional designs, such as 3D-printed high-force artificial muscles, and antagonistic muscle pairs, further demonstrate the versatility and scalability of the approach.
The talk concludes with perspectives on how self-contained pneumatic systems can enable new frontiers in soft robotics beyond the laboratory air line.

Dr. Erhan Oztop, Professor, Osaka University, Japan

Biography: Erhan Oztop received the Ph.D. degree from the University of Southern California, in 2002. In the same year, he joined Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Japan. There, he was a Researcher and later a Senior Researcher and the Group Leader, in addition to serving as the Vice Department Head for two research groups and holding a visiting associate professor position with Osaka University, from 2002 to 2011. Currently, he is a Professor with Özyeğin University, Turkey, and a Specially Appointed Professor with Osaka University, Japan. His research interests include computational modeling intelligent behavior, machine learning, cognitive and developmental robotics, and cognitive neuroscience and human-robot adaptation.

Speech Title: Efficient Robot Learning from Demonstration 

Abstract: Learning from Demonstration (LfD) enables robots to acquire movement primitives directly from human demonstrations. Recent LfD models leverage deep learning to construct expressive and powerful movement representations. However, amid the rapid progress in AI and deep learning, the resource aspect of such systems are often overlooked. Yet, many application domains still require lightweight and efficient solutions, such as low-power devices, edge computing, and on-board robot learning.
In this talk, I will first outline our work on representing motor primitives using an efficient alternative to deep learning, namely, reservoir computing. I will then introduce the idea that incorporating structural biases aligned with LfD data can both reduce computational requirements and improve the accuracy of learning robot skills with deep neural networks.

Dr. Toshiaki Tsuji, Associate Professor, Saitama University, Japan

Biography:Toshiaki Tsuji received the B.E. degree in system design engineering and the M.E. and Ph.D. degrees in integrated design engineering from Keio University, Yokohama, Japan, in 2001, 2003, and 2006, respectively. He was a Research Associate with the Department of Mechanical Engineering,Tokyo University of Science, from 2006 to 2007. He is currently an Associate Professor with the Department of Electrical and Electronic Systems, Saitama University, Saitama, Japan. He has been working to enhance robotic skills and has advanced research on force measurement and force control, as well as their applications to manipulation. He has pursued exploration of methods for acquiring latent representations of force and position for modeling skilled movements, and received the Nagamori Award in 2025.He also received the RSJ Advanced Robotics Excellent Paper Award and the IEEJ Industry Application Society Distinguished Transaction Paper Award in 2020.

Speech Title: Imitation Learning of Contact-rich Tasks

Abstract: Contact-rich tasks, which require complex physical interactions with the environment, are an important challenge in robotics due to their nonlinear dynamics and sensitivity to small environmental deviations. Imitation learning is a key technology for acquiring human motor skills through robots to advance contact-rich manipulation. Moreover, with recent advances in large language models, its application scope is likely to expand significantly in the future. This talk introduces imitation learning approaches, highlighting their applications to contact-rich manipulation.