1st International Conference on

Machine Intelligence

May 20 – 21, 2019 Kitakyushu International Conference Center Kitakyushu, Japan

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Contents

General Information 2

Schedule 4

Keynote Talks 7

Venue and Directions 21

Organizing Committee Yasunori Mitani, Kyushu Institute of Technology, Japan Kazuo Ishii, Kyushu Institute of Technology, Japan Hidenori Matsuura, YASKAWA Electric Corporation, Japan Hyoungseop Kim, Kyushu Institute of Technology, Japan Eiji Hayashi, Kyushu Institute of Technology, Japan Joze Guna, University of Ljubljana, Slovenia Huimin Lu, Kyushu Institute of Technology, Japan Yuya Nishida, Kyushu Institute of Technology, Japan Ryusuke Fujisawa, Kyushu Institute of Technology, Japan Shinsuke Yasukawa, Kyushu Institute of Technology, Japan Iztok Humar, University of Ljubljana, Slovenia Daniel Skočaj, University of Ljubljana, Slovenia Matej Kristan, Univesity of Ljubljana, Slovenia Marko Munih, University of Ljubljana, Slovenia Niko Herakovic, University of Ljubljana, Slovenia

Secretariat

Research Cooperation Division at Kyushu Institute of Technology.

Additional Contact Information For general questions or concerns, please email icmi@jimu.kyutech.ac.jp. For emergencies, call the Japan Police at 110.

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General Information

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Program Registration Program registration will take place between 8:30 am – 9:30 am on Monday, May 20, 2019.

The registration desk will be located in the gate of the International Conference Room, on

the 2nd floor of the conference center. There will be signage posted around the building to

help direct you to the program registration. Please arrive on time as the program will start

promptly at 9:30 am in the International Conference Room.

Please see the Venue and Directions section for maps and more detailed directions.

Lunch Break Lunch (Italian food) is provided during the conference. Please contact the organizing

committee, if you want to choose other foods.

Internet Access Wireless internet access is available in the International Conference Room.

Participants can connect to the following guest networks:

・ID: icmi2019

・PWD: kyutech2world

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Schedule

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Day 1 – May 20. Location: International Conference Room 8:30am-9:30am Registration

9:30am-9:45am Welcome Ceremony

Prof. Yuji Oie

9:45am-10:00am Introduction to Slovenia

Mr. Matjaž Ingolič (Embassy of Slovenia in Japan)

10:00am-10:20am Group Photo/Coffee Break

10:20am-10:50am Haptics for Rehabilitation Robotics and Teleoperation

Prof. Marko Munih (University of Ljubljana, Slovenia)

10:50am-11:20am Introduction of YASKAWA Electric Corporation and the Development

of Next-Generation Robots

Mr. Yukihiro Tobata (YASKAWA Electric Corporation, Japan)

11:20am-11:50am MY VISION: A Support System Providing Visual Assistance

Prof. JooKooi Tan (Kyushu Institute of Technology, Japan)

11:50am-1:00pm Lunch

1:00pm-1:30pm Deep-learning-based Segmentation for Various Computer Vision

Tasks

Prof. Daniel Skočaj (University of Ljubljana, Slovenia)

1:30pm-2:00pm Bio-inspired Image Processing for Seafloor Image Enhancement and

Selection

Prof. Kazuo Ishii (Kyushu Institute of Technology, Japan)

2:00pm-2:30pm Lightweight Networks for Real-time Semantic Segmentation

Prof. Quan Zhou (Nanjing Univ. of Posts and Telecom., China)

2:30pm-3:00pm Discriminative Correlation Filters for Short and Long-term Tracking

Prof. Matej Kristan (University of Ljubljana, Slovenia)

3:00pm-3:30pm Representation Learning for Face Image Analysis

Prof. Guangwei Gao (Nanjing Univ. of Posts and Telecom., China)

3:30pm-3:40pm Coffee Break

3:40pm-4:10pm Applications of Cognitive computing in Communications and

Services

Prof. Iztok Humar (University of Ljubljana, Slovenia)

4:10pm-4:40pm Exploiting Low-Dimensionality in Human and Robot Behavior for

Advanced Assistive Robotics

Prof. Tomohiro Shibata (Kyushu Institute of Technology, Japan)

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4:40pm-5:10pm VR/AR/MR - Technologies, Trends and Challenges

Prof. Jože Guna (University of Ljubljana, Slovenia)

5:10pm-5:55pm Student Poster Session

5:55pm-6:00pm Closing Ceremony

6:30pm Social Dinner (@RIHGA Royal Hotel Kokura)

6:30pm Welcome Message

Prof. Yasunori Mitani

6:32pm Toast

Prof. Seiichi Serikawa

8:30pm Closing Remarks

Prof. Teruhisa Ohno

Day 2 – May 21. Location: Kyutech Wakamatsu Campus & YASKAWA Electric Corporation 8:30am Pickup at Kyutech Tobata Campus (West Gate)

9:00am-9:10am Pickup at JR Kokura Station (Shinkansen Exit)

9:15am-11:45am Open Lab Hours/Japan-Slovenia Workshop (Invitation ONLY)

11:45am-12:30am Lunch

1:10pm-2:50pm YASKAWA Electric Corporation

3:30pm Arrive at Kyutech Tobata Campus

3:40pm-4:00pm Meeting for Fostering Joint International Research (Invitation ONLY)

6:00pm-8:00pm Dinner (Invitation ONLY)

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Invited Talks

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Prof. Yuji Oie

President, Kyushu Institute of Technology

Prof. Yasunori Mitani

Vice President, Kyushu Institute of Technology

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Introduction to Slovenia Mr. Matjaž Ingolič (Minister Counsellor, Embassy of the Republic of Slovenia in Japan)

Lake Bled

Ljubljana City

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Haptics for Rehabilitation Robotics and Teleoperation Prof. Marko Munih (University of Ljubljana, Slovenia)

Greek word ‘απτω’ can represent grasping, holding or touching, in robotics is used for sense of touch by applying forces, vibrations or motions to the user. The human is holding the robot tip, and the force control algorithms are implemented instead of traditional position control. The robot control moderates the interaction force based on current position, velocity and acceleration of interaction point. Haptic robots are mostly used to assist in creation of virtual objects in various fields of computer virtual reality, including rehabilitation robotics and in telemanipulation. The first case in presentation is robot-assisted rehabilitation of upper extremities. The therapy robot works in combination with immersive virtual reality systems including 3D graphics and 3D sound. Several individually adjustable scenarios are possible in order to increase the motivation of the patients and, thus, the therapeutic outcome of the training. The second example is bimanual teleoperation system consisting of four robots: two Omega.7 haptic interfaces as master devices and two Motoman MH5 manipulators. The setup enables scaling of forces and positions between master and slave devices. Robots are using Robolab originated open architecture xPCtarget/Matlab/Simulink environment, suitable for complex algorithms, for visual servoing, force/torque control, sensor integration, human-robot interaction, teleoperation, haptics and other specific approaches that cannot run on standard industrial controllers. Controller design makes it suitable for research and education as well as for prototyping of complex industrial applications.

Marko Munih is a Full Professor, Head of Robolab at University of Ljubljana since 1997. His early research interests were in functional electrical stimulation, including measurements, control, biomechanics and electrical circuits. Further focus is on robot contact with environment, use of haptic interfaces in the fields of industry and rehabilitation engineering, in combination with VR. Notable impact had research on physiological computing linked to rehabilitation robotics.

Build was robot for construction and applications of robots for measurement tasks, recently also research of contactless measurements of kinematics with Inertial Measurement Units (IMU) in exoskeletons and prosthetic devices. Among all these fields he was/is partner PI in ten EU projects, as well as lead direct industry funded projects. He is author/co-author of 140 peer reviewed journal scientific publications, five patents and four university books. Recognitions include the Zois (2002), Vidmar (2011) and Vodovnik (2012) awards, and Gold plaquette from UL in 2015 for research and university activities. With colleagues was recipient of EUROP/EURON Robotics Technology Transfer Award, 3rd prize (2010) and UL Rector Award for 2nd best innovation at University of Ljubljana (2012).

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Introduction of YASKAWA Electric Corporation and the Development of Next-Generation Robots Mr. Yukihiro Tobata (YASKAWA Electric Corporation, Japan)

The YASKAWA Electric Corporation is a Japanese manufacturer of servos, motion controllers, AC motor drives, switches and industrial robots. Their Motoman robots are heavy duty industrial robots used in welding, packaging, assembly, coating, cutting, material handling and general automation. The company was founded in 1915, and its head office is located in Kitakyushu, Fukuoka Prefecture. YASKAWA applied for a trademark on the term "Mechatronics" in 1969, it was approved in 1972. The head-office, in Kitakyushu, was designed by the American architect Antonin Raymond in 1954.

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MY VISION: A Support System Providing Visual Assistance Prof. JooKooi Tan (Kyushu Institute of Technology, Japan)

MY VISION is a system focusing on the third (virtual) eye of a person for recognition and analysis of objects in a real environment. The system acquires certain visual information useful for human daily activities, especially for a visually or an orally impaired person. Three types of MY VISION are currently under development, i.e., a navigation system for a visually impaired person to walk on a road and cross a crossroad safely by discriminating obstacles and pedestrians surrounding him/her and finding traffic signals as well as a crossroad on outdoor walk, (ii) a public bus finding system for a visually impaired person when going out, and (iii) a mobile communication system for an orally impaired person to realize smooth transmission of his/her intension to anyone. In this talk, recent technical achievements on MY VISION will be introduced.

Joo Kooi Tan obtained the Ph.D. from Kyushu Institute of Technology. She is presently with Department of Mechanical and Control Engineering in the same university as Associate Professor. Her current research interests include three-dimensional shape/motion recovery, human detection and its motion recognition from a video. She was awarded SICE Kyushu Branch Young

Author’s Award in 1999, the AROB Young Author’s Award in 2004, Young Author’s Award from IPSJ of Kyushu Branch in 2004 and BMFSA Best Paper Awards in 2008, 2010, 2013 and 2015.

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Deep-learning-based Segmentation for Various Computer Vision Tasks Prof. Danijel Skočaj (University of Ljubljana, Slovenia)

In recent years, we have witnessed a significant progress in the field of computer vision, mainly due to the success of deep learning methods and application of convolutional neural networks to various computer vision tasks. In this talk we will focus on deep-learning-based image segmentation and related approaches. We will present a deep model for addressing the problem of surface-defect detection in visual inspection using a segmentation-based deep architecture. The developed solution replaces the traditional machine vision hand-crafted engineering approach with a data-driven learning-based method. We will present also solutions to several other computer vision problems that are based on image segmentation, and deep architectures that have been developed to solve these problems. One of their most crucial architectural elements is the effective receptive field size, that has to be manually set to accommodate a specific task. We address this issue by proposing a new convolution filter composed of displaced aggregation units (DAU). DAUs learn spatial displacements and adapt the receptive field sizes of individual convolution filters to a given problem, thus eliminating the need for hand-crafted modifications, and are therefore particularly suited to address the segmentation-based problems.

Danijel Skočaj is an associate professor at the University of Ljubljana, Faculty of Computer and Information Science. He is the head of the Visual Cognitive Systems Laboratory. He has obtained the Ph.D. in computer and information science from the University of Ljubljana in 2003. His main research interests lie in the fields of computer vision, machine learning, and cognitive robotics; he is

involved in basic and applied research in visually enabled cognitive systems, with emphasis on visual learning, recognition, and segmentation. He is also interested in the ethical aspect of artificial intelligence. He has lead or collaborated in a number of research projects, such as EU projects (CogX, CoSy, CogVis), national projects (DIVID, GOSTOP, VILLarD), and several industry-funded projects. He has developed a strong collaboration with researchers from many research institutions from all around Europe; he was also a visiting researcher at University of Birmingham. He has rich teaching and supervision experience supervising a number of undergraduate and PhD students. He served as the president of the IEEE Slovenia Computer Society, and the president of the Slovenian Pattern Recognition Society.

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Bio-inspired Image Processing for Seafloor Image Enhancement and Selection Prof. Kazuo Ishii (Kyushu Institute of Technology, Japan)

Ocean exploration is one of important missions for human because to find new resource of energy, rare metals, and unknown creatures. The ocean is the last frontier and extreme environment to prevent human from direct access by high pressure, darkness, radio attenuation and so on. We have been developing AUVs: Autonomous Underwater Vehicles as the tool for ocean observation. Underwater imaging is one of research topics for AUVs, and we have introduced bio-inspired technologies, Retinex model for seafloor image enhancement and Saliency map for image selection. We show the image enhancement and selection for underwater images and the results of sea trials. *This work is coauthored by Kazuo Ishii, Ahn Johnghyun, Shinsuke Yasukawa, Yuya Nishida, Takashi Sonoda, Keisuke Watanabe

Kazuo Ishii received the Ph.D. degree from the Department of Naval Architecture and Ocean Engineering, University of Tokyo,

Tokyo, Japan, in 1996. He is currently a Professor with the

Department of Human Intelligence Systems, and the Director of the

Center for Socio-Robotic Synthesis, Kyushu Institute of Technology,

Kitakyushu, Japan. His research interests include underwater

robots, agricultural robots, roboCup soccer robots, and intelligent

systems.

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Lightweight Networks for Real-time Semantic Segmentation Prof. Quan Zhou (Nanjing Univ. of Posts and Telecom., China)

Existing methods for semantic segmentation either pay more attention on segmentation accuracy without considering implementing efficiency, or emphasize more on high-speed inference, neglecting producing high-accuracy segmentation outputs. In recent years, the advance of deep convolutional neural networks (CNNs) makes remarkable progress on semantic segmentation, but the effectiveness of these networks largely depends on the sophisticated model design regarding depth and width, which has to involve many operations and parameters. In the talk, I will briefly overview the recent advances in building lightweight networks for real-time semantic segmentation. Recent CNN-based efforts are mainly categorized into two categories: network compression, and convolution factorization. Where the first one prefers to reduce inference computation by compressing pre-trained networks, and in contrast, the second one focuses on directly training network with smaller size. After given some representative lightweight networks, we will introduce two Encoder-Decoder architectures that aim to develop efficient residual networks for real-time semantic segmentation. We call them LEDNet and ESNet, respectively, where the encoder network is utilized to abstract image features and the decoder counterpart is employed to sequentially recover image details.

Quan Zhou is an Associate Professor at the National Engineering Research Center of Communications and Networking Technology (NERCCN), College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications. Now he is the director of Key Laboratory of Broadband Wireless Communication and Sensor Network Technology. He received the B.S. degree in electronic and information engineering from China Geosciences University in 2002, Wuhan. He achieved the M.E. degree and the Ph.D. degree in electronic and information

engineering from Huazhong University of Science and Technology, Wuhan, in 2006 and 2013, respectively. He was a Visiting Scholar at Umeå University of Sweden from April 2015 to June 2015. From July 2017 to August 2017, he also visited Kyushu Institute of Technology in Japan. His research interests include machine learning, pattern recognition and computer vision, especially in image understanding and semantic segmentation, saliency detection and visual attention, and face detection and recognition. He has published over 40 technical papers at core journals and important international conferences, including IEEE Transactions on Image Processing, Pattern Recognition, IEEE ICASSP, IEEE ICIP, ICPR and ACCV. He has been invited as the chairman and the technical committees member of a series of internationally renowned academic conferences and forums such as IEEE ICME, WCSP, ICONIP, ISAIR, ROSENET, etc. He is a member of the IEEE and IAPR.

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Discriminative Correlation Filters for Short and Long-term Tracking Prof. Matej Kristan (University of Ljubljana, Slovenia)

Visual object tracking has been enjoying a significant interest of the research community for over several decades due to scientific challenges it presents and its large practical potential. In its most general formulation, it addresses localization of an arbitrary object in all frames of a video, given a single annotation specified in one frame. In recent years, discriminative correlation filters (DCF) have become a dominant tracking methodology due to their mathematical elegance and efficient implementation. One deficiency of DCFs is a requirement that the size of the filter and the search region to be equal, which limits the training and searching capabilities. Another deficiency is that feature selection in multichannel filter is biased by the feature channel energy, which does not necessarily reflect the discriminative power. Most of DCFs trackers lack the ability for target re-detection – a long-term property. Furthermore, the DCFs inherently consider the target as a 2D object and cannot distinguish the appearance changes due to out-of-plane rotation from partial occlusion. In this talk, I will first present a DCF tracker CSRDCF, that addresses learning from a larger region and channel fusion within a single formulation. Then I will describe how this formulation allows a straightforward extension to long-term tracking, leading to a FuCoLoT tracker. Lastly, I will discuss a tight coupling with a 3D model generation that pushes DCF general object tracking towards a model-free 6DoF tracking.

Matej Kristan is an Associate professor at University of Ljubljana. He holds undergraduate, MPhil and PhD degrees in Electrical Engineering from University of Ljubljana, Slovenia. He has spent time as a visiting researcher at Birmingham University, UK, and as a visiting professor at Tampere University of Technology, Finland. His research interests include probabilistic methods for computer vision with focus on visual tracking, dynamic models, online learning,

object detection and vision for mobile robotics. He has over 150 publications in journals and conferences, according to Google Scholar, his h-index is 26 and his work has been cited 3267 times. Since 2013, Prof. Kristan has been a co-organizer of the largest international initiative in visual object tracking VOT, which is widely recognized authority in visual object tracking performance evaluation.

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Representation Learning for Face Image Analysis Prof. Guangwei Gao (Nanjing Univ. of Posts and Telecom., China)

During the collection, transmission and storage of image data, due to the distance between the individual and the acquisition equipment, the imaging quality of the acquisition equipment, and the space limitations of the storage equipment, inevitably low resolution, blurring and other degradation phenomena will occur. Image applications such as image recognition require high quality images. In this talk, I will first try to establish a theoretical and algorithmic framework for image quality enhancement through the analysis and processing of low-quality images for subsequent image recognition. Then, in face of big data, I will technically design a fast and effective image recognition algorithm to improve the computer's ability to identify large-scale data. Last, based on the theoretical analysis and algorithm design, I will try to establish an integrated framework for image restoration and image recognition, making the entire pattern recognition system achieve the best recognition performance.

Guangwei Gao is an associate professor in the Institute of Advanced Technology, Nanjing University of Posts and Telecommunications. He received the B.S. degree in information and computation science from Nanjing Normal University, Nanjing, China, in 2009, and the Ph.D. degree in pattern recognition and intelligence systems from Nanjing University of Science and Technology, Nanjing, China, in 2014. From

March 2011 to September 2011 and February 2013 to August 2013, he was an exchange student of Department of Computing, Hong Kong Polytechnic University. His research interests include pattern recognition, image processing, and especially the applications to face recognition and face hallucination. He has published over 40 technical papers at prestigious international journals and conferences. He was the technical committee member of several international conferences, such as ISAIR2018, WCSP2017, ISAIR2017, ICONIP2017, ACPR2017, etc. He is now a member of the IEEE, CCF, and CAAI.

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Applications of Cognitive computing in Communications and Services Prof. Iztok Humar (University of Ljubljana, Slovenia)

In order to achieve better quality of experience and higher energy efficiency in modern communications networks and to bring the intelligence to their services, the edge computing has to be upgraded with cognitive computing abilities. Interconnecting edge computing, data resources and network resources expands computing resources, network bandwidth, and storage capacity of the cloud platform. With the development of artificial intelligence, cloud/edge computing and 5G network slicing, more intelligent networks are under deliberation. Introducing cognitive architectures, these approaches can be applied in many areas, including the intelligent household, advanced healthcare, automatic driving, emotional interactions and intelligent manufacturing. This talk discusses the advances of such networks through several researches performed together with the group of prof. Min Chen, EPIC Lab, School of Computer Science and Technology at Huazhong University of Science and Technology. Extending the traditional architecture with the following layers: sensing, communication, cognition, control and application layer is a key enabling technology in mining effective information from physical, network and application data space. This enables new approaches on different communications layers: from the intelligent services in hybrid low power wide area networks and dynamic service migration mechanisms in edge computing on the network layer to the intelligent services on application layer, such as cognitive internet of vehicles, intelligent factory and cognitive smart-homes.

Iztok Humar received Ph.D. degrees in telecommunications from the Faculty of Electrical Engineering (FE) and information management at the Faculty of Economics, University of Ljubljana, Slovenia, in 2007 and 2009, respectively. He is an associate professor at the FE, where he lecturers on fundamentals of electrical engineering and on the design, management and modeling of communication networks. His main research topics include the design, planning and management of communications networks and services, and edge

cognitive computing and modeling of networks and traffic for energy efficiency and QoS/QoE. In 2010, he was a three-month Visiting Professor and Researcher at Huazhong University of Science and Technology, Wuhan, China and they continue to perform joint research work through long visiting periods from 2017 to 2020. He is a Senior member of IEEE Communication Society from 2010 and a member of Electrical Association of Slovenia. He served as IEEE Communication Society of Slovenia Chapter Chair for 10 years.

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Exploiting Low-Dimensionality in Human and Robot Behavior for Advanced Assistive Robotics Prof. Tomohiro Shibata (Kyushu Institute of Technology, Japan)

The recent demographic trend across developed nations shows a dramatic increase in the ageing population, fallen fertility rates and shortage of caregivers, which has been the most salient in Japan. Hence, the demand for service robots to assist daily lives is increasing. Such a robot needs to be easily used, and to deal with an individual’s different characteristics, e.g., in kinematics and muscles, which can generally continue to change over time. Since the human body has high degrees of freedom (DOFs), and also assistive robots often have high DOFs, finding and utilizing their low-dimensionality are very important to enable easy-to-use and individual adaptivity. In this talk, I will introduce my studies on advanced assistive robotics where low-dimensionality in human and robot behavior were exploited.

Tomohiro Shibata received a Ph.D. from the University of Tokyo, Japan in 1996, continued his robotics study as a JSPS researcher, and then worked on computational neuroscience research at ATR as a JST researcher. After working as an associate professor at Nara Institute of Science and Technology, he currently works as a professor at Kyushu Institute of Technology, Kitakyushu, Japan. He

is a member of the working group for the national strategic special zone in Kitakyushu focusing on nursing-care robots. He was an editorial board member of Neural Networks and an executive board member of the Robotics Society of Japan (RSJ). He is currently an executive board member of Japanese Neural Network Society, a committee member of RSJ for international affairs, and a governing council member of The Robotics Society (former Robotics Society of India).

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VR/AR/MR - Technologies, Trends and Challenges Prof. Joze Guna (University of Ljubljana, Slovenia)

XReality (XR or Cross Reality) technologies combining virtual and real world experiences, are emerging as a mature and affordable technology. The term XR encompasses a wide spectrum of hardware and software, including sensory interfaces, applications, and infrastructures, that enable content creation for virtual reality (VR), mixed reality (MR), augmented reality (AR) solutions. These, mixed, real, augmented and virtual technologies present a new medium for expression and exploring of new, innovative concepts and ideas. There are some challenges still, mainly stemming from too much focus on the technology itself and not enough focus on the people – the users of these systems. Main challenges stem from current technology limitations and result in suboptimal user experience, such as the VR sickness/cybersickness phenomena. New technological solutions are needed to provide an intuitive and natural user experience and interactions. The talk will therefore focus on the current state-of-the-art overview of the XR technologies and its applications, illuminate the challenges and provide possible solutions with future trends.

Jože Guna is an Assistant Professor at the Faculty of Electrical Engineering, University of Ljubljana. His area of research focuses on Internet technologies, multimedia technologies and IPTV systems with special emphasis on user centred design, user interaction modalities and designing the user experience, VR/AR/MR technologies, including gamification and flow aspects.

Currently he is involved in a number of projects focusing on the development of intuitive user interfaces for elderly users of eHealth application and interactive multimedia HBBTV and VR/AR/MR applications. He is an expert in Internet, ICT and IPTV technologies and holds several industrial certificates from CISCO, Comptia and Apple, including trainer licenses from Cisco and Apple. He is a senior member of the IEEE organization and IEEE Slovenia Section Secretary General.

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Venue and Directions

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Kitakyushu International Conference Center (MICE), Kitakyushu (北九州国際会議場)

Address: 3 Chome-9-30 Asano, Kokurakita Ward, Kitakyushu, Fukuoka Prefecture 802-0001, Japan (5 min. walk from JR Kokura Sta. Shinkansen Exit) Please contact us at icmi@jimu.kyutech.ac.jp for Japan VISA affairs.

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Social Dinner

Monday, May 20, 6:30pm~8:30pm

The RIHGA Royal Hotel Kokura is located in Kokura, Kyushu and directly accessible from Kokura Station on the Shinkansen bullet train.

Address:2-14-2 Asano, Kokurakita-ku, Kitakyushu,

Fukuoka 802-0001 Japan

Phone: +81 (0)93-531-1121

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Acknowledgements Thanks the Research Cooperation Division and International Affairs Division at Kyushu Institute of Technology for their financial support and assistance. We also would like to thank the Kitakyushu City, Kitakyushu International Conference Center (MICE), YASKAWA Electric Corporation and International Society for Artificial Intelligence and Robotics (ISAIR) for their kind help.