Plenary Sessions

Designing Effective Multi-objective Particle Swarm Optimization Algorithms
A/Prof. K C Tan, National University of Singapore

Autonomous Physical Interaction combining Vision, Tactile and Force Feedback
Professor Angel P. del Pobil,Professor, Universitat Jaume I
Director, UJI Robotic Intelligence Laboratory
WCU Visiting Professor, Sungkyunkwan University
Board Member, European Robotics Research Network

Evolutionary Computation techniques for Computer Vision
Prof Mengjie Zhang
Victoria University of Wellington

Title: Designing Effective Multi-objective Particle Swarm Optimization Algorithms

Presenter: A/Prof. K C Tan, National University of Singapore

Multi-objective (MO) optimization is a challenging research topic because it involves the simultaneous optimization of several conflicting objectives in the Pareto optimal sense and requires researchers to address many issues that are unique to MO problems. Multi-objective particle swarm optimization (MOPSO) algorithm is a stochastic optimization technique that simulates the behavior of bird flocks to solve MO problems. The MOPSO algorithm is steadily gaining attention from the research community in evolutionary computation due to its fast convergence, which on the other hand, also gives rise to various design issues such as complexity and effectiveness. In this talk, a number of advanced features for MOPSO and co-evolutionary algorithms are proposed for handling large-scale optimization problems. A couple of industrial applications will also be presented.

Kay Chen TAN is currently an Associate Professor in the Department of Electrical and Computer Engineering, National University of Singapore. He is actively pursuing research in computational and artificial intelligence, with applications to multi-objective optimization, scheduling, automation, data mining, and games.

Dr Tan has published over 100 journal papers, over 100 papers in conference proceedings, co-authored 5 books including Multiobjective Evolutionary Algorithms and Applications (Springer-Verlag, 2005), Modern Industrial Automation Software Design (John Wiley, 2006; Chinese Edition, 2008), Evolutionary Robotics: From Algorithms to Implementations (World Scientific, 2006; Review), Neural Networks: Computational Models and Applications (Springer-Verlag, 2007), and Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms (Springer-Verlag, 2009), co-edited 4 books including Recent Advances in Simulated Evolution and Learning (World Scientific, 2004), Evolutionary Scheduling (Springer-Verlag, 2007), Multiobjective Memetic Algorithms (Springer-Verlag, 2009), and Design and Control of Intelligent Robotic Systems (Springer-Verlag, 2009).

Dr Tan has been invited to be a keynote/invited speaker for 21 international conferences. He served in the international program committee for over 100 conferences and involved in the organizing committee for over 30 international conferences, including the General Co-Chair for IEEE Congress on Evolutionary Computation 2007 in Singapore and the General Co-Chair for IEEE Symposium on Computational Intelligence in Scheduling 2009 in Tennessee, USA. Dr Tan is currently a Distinguished Lecturer of IEEE Computational Intelligence Society.

Dr Tan is currently the Editor-in-Chief of IEEE Computational Intelligence Magazine (CIM). He also serves as an Associate Editor / Editorial Board member of over 15 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Computational Intelligence and AI in Games, Evolutionary Computation (MIT Press), European Journal of Operational Research, Journal of Scheduling, and International Journal of Systems Science.

Dr Tan is the awardee of the 2012 IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award for his contributions to evolutionary computation in multi-objective optimization. He also received the Recognition Award (2008) from the International Network for Engineering Education & Research (iNEER) for his outstanding contributions to engineering education and research. He was also a winner of the NUS Outstanding Educator Awards (2004), the Engineering Educator Awards (2002, 2003, 2005), the Annual Teaching Excellence Awards (2002, 2003, 2004, 2005, 2006), and the Honour Roll Awards (2007). Dr Tan is currently a Fellow of the NUS Teaching Academic.

Title: Autonomous Physical Interaction combining Vision, Tactile and Force Feedback

The talk will be based on my latest book titled Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback: Applications to Assistive Robotics, to be published in the Springer Tracts in Advanced Robotics (STAR) series, co-authored by Mario Prats and Pedro J. Sanz. This research was recipient of various awards, including the Georges Giralt European Award and the Robotdalen Scientific Award Honorary Mention.

Autonomous robot manipulation is one of the most important challenges in robotics. It involves three challenges: versatility, defined as the capability to adapt to different situations, instead of being limited to a particular task; autonomy, that concerns the level of independence in the robot operation, and dependability, that refers to the capability of successfully completing an action even under important modeling errors or inaccurate sensor information. A complete manipulation task involves two sequential actions: that of achieving a suitable grasp or contact configuration, and the subsequent motion required by the task. We propose a unified framework with the introduction of task-related aspects into the classical knowledge-based grasp concept, leading to task-oriented grasps. In a similar manner, grasp-related issues are also considered during the execution of a task, leading to grasp-oriented tasks. We call this unified representation physical interaction. In the talk I will first present a theoretical framework for the integrated specification of physical interaction tasks, supporting a great variety of actions. Next, the problem of autonomous planning of physical interaction tasks will be addressed. I will then focus on the dependable execution of these tasks, and adopt a sensor-based approach with three different types of sensor feedback: force, vision and tactile. The methods proposed provide important advances with respect to the state-of-the-art versatility, autonomy and dependability of robotic manipulation, allowing to address a wide range of tasks. All these contributions are validated with several experiments using different real robots placed on household environments.

Professor Angel P. del Pobil

Professor, Universitat Jaume I
Director, UJI Robotic Intelligence Laboratory
WCU Visiting Professor, Sungkyunkwan University
Board Member, European Robotics Research Network

Angel P. del Pobil is Professor of Computer Science and Artificial Intelligence at Jaume I University (Spain), founder director of the UJI Robotic Intelligence Laboratory, and a WCU Visiting Professor at Sungkyungkwan University (Korea). He holds a B.S. in Physics (1986) and a Ph.D. in Engineering (1991), both from the University of Navarra. His Ph.D. Thesis was the winner of the National Award of the Spanish Royal Academy of Doctors. He has been Co-Chair of two Technical Committees of the IEEE Robotics and Automation Society, and he is Board member of EURON, the European Robotics Research Network, since 2001. He has been EURON Co-Chair for Research, and Vice President of the International Society of Applied Intelligence. He has over 200 refereed publications, including ten books. Prof. del Pobil was organizer of some 37 workshops and tutorials at ICRA, IROS, RSS, HRI and other major conferences. He was Program Co-Chair of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence, and General Chair of five editions of the International Conference on Artificial Intelligence and Soft Computing (2004-2008). He is Associate Editor for ICRA (2009-2011) and IROS (2007-2011) and has served on the program committees of over 100 international conferences. He has been involved in robotics research for the last 25 years; his research interests include: humanoid robots, service robotics, mobile manipulation, visually-guided grasping, robot perception, multimodal sensorimotor transformations, robot physical and human interaction, robot learning, developmental robotics, and the interplay between neurobiology and robotics. Professor del Pobil has been invited speaker of 49 tutorials, plenary talks, and seminars in 14 countries. He serves as associate or guest editor for seven journals, and as expert for the European Commission and the National Science Foundation. He has supervised 14 Ph.D. thesis, including winner and finalists of international awards. He has been Principal Investigator of 28 research projects. Recent projects at the UJI Robotic Intelligence Lab funded by the European Commission include: GUARDIANS (Group of Unmanned Assistant Robots Deployed In Aggregative Navigation supported by Scent detection), EYESHOTS (Heterogeneous 3-D Perception Across Visual Fragments), and GRASP (Emergence of Cognitive Grasping through Emulation, Introspection, and Surprise).

Title: Evolutionary Computation techniques for Computer Vision


Computer vision is an interdisciplinary field concerned with the automated processing and analysis of images from the real world. The applications range from simple edge detection tasks to complex robot vision tasks. Due to the large search space and noise environments, solving vision tasks has been very challenging. Over the last decade there has been increasing interest in using evolutionary computation techniques to solve vision problems. This talk will discuss how to use evolutionary techniques such as genetic programming and learning classifier systems to solve vision problems particularly image classification tasks. Three real world applications in automatic object tracking, motion detection and adaptive digit recognition will also be discussed. A key characteristic of this approach to these challenging problems is that one can usually obtain exciting results without needing to develop complex mathematical models.

Prof Mengjie Zhang

Mengjie Zhang is currently a Professor/Reader in Computer Science at Victoria University of Wellington, where he heads the Evolutionary Computation Research Group. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of computer vision and image processing, multi-objective optimisation, classification with unbalanced data, and feature selection and dimension reduction for classification with high dimensions. He is also interested in data mining, machine learning, and web information extraction.

Dr Zhang has published over 180 academic papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for five international journals including IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal (MIT Press), and as a reviewer of over 15 international journals. He has been a program/technical/special session co-chair for five international conferences. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top ten international genetic programming researchers by the GP bibliography (

Dr Zhang is a senior member of IEEE, a member of the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and a committee member of the IEEE New Zealand Central Section. He has been a main organizer of the special session of Evolutionary Computer Vision at IEEE Congress on Evolutionary Computation since 2005.

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