RoboCup@Home aims to develop service robots for personal domestic use through competitions evaluating robots' capabilities in realistic home environments. The document discusses service robotics research and development (R&D) focusing on an educational robotics initiative using open robot platforms to promote robotics education through competitions and outreach programs.
A cognitive robot equipped with autonomous tool innovation expertise IJECEIAES
Like a human, a robot may benefit from being able to use a tool to solve a complex task. When an appropriate tool is not available, a very useful ability for a robot is to create a novel one based on its experience. With the advent of inexpensive 3D printing, it is now possible to give robots such an ability, at least to create simple tools. We proposed a method for learning how to use an object as a tool and, if needed, to design and construct a new tool. The robot began by learning an action model of tool use for a PDDL planner by observing a trainer. It then refined the model by learning by trial and error. Tool creation consisted of generalising an existing tool model and generating a novel tool by instantiating the general model. Further learning by experimentation was performed. Reducing the search space of potentially useful tools could be achieved by providing a tool ontology. We then used a constraint solver to obtain numerical parameters from abstract descriptions and use them for a ready-to-print design. We evaluated our system using a simulated and a real Baxter robot in two cases: hook and wedge. We found that our system performs tool creation successfully.
The ability of intuition and self- learning in humans is responsible for developing their
intelligence, reasoning and socialising. All this human characteristics can enable the robots to
volve into humans. In this context i explain that robots with developing intelligence can solve the problems of various scientific phenomenon such as black-hole, time travels and even in robotics the problems in sensors and actuators which do not impart human level DOF and movement thus making them do everything we can do. Imagine a robot doing yoga, karate, even a ballet all by itself without the rusty old controls and commands. Researchers have come with all kinds of robots and best of all social robots for social interaction so we have come with all kinds of robots what’s next? Robot scientists and researchers! Why not? It is highly evident that robot can think in new dimensions to solve issues.
A cognitive robot equipped with autonomous tool innovation expertise IJECEIAES
Like a human, a robot may benefit from being able to use a tool to solve a complex task. When an appropriate tool is not available, a very useful ability for a robot is to create a novel one based on its experience. With the advent of inexpensive 3D printing, it is now possible to give robots such an ability, at least to create simple tools. We proposed a method for learning how to use an object as a tool and, if needed, to design and construct a new tool. The robot began by learning an action model of tool use for a PDDL planner by observing a trainer. It then refined the model by learning by trial and error. Tool creation consisted of generalising an existing tool model and generating a novel tool by instantiating the general model. Further learning by experimentation was performed. Reducing the search space of potentially useful tools could be achieved by providing a tool ontology. We then used a constraint solver to obtain numerical parameters from abstract descriptions and use them for a ready-to-print design. We evaluated our system using a simulated and a real Baxter robot in two cases: hook and wedge. We found that our system performs tool creation successfully.
The ability of intuition and self- learning in humans is responsible for developing their
intelligence, reasoning and socialising. All this human characteristics can enable the robots to
volve into humans. In this context i explain that robots with developing intelligence can solve the problems of various scientific phenomenon such as black-hole, time travels and even in robotics the problems in sensors and actuators which do not impart human level DOF and movement thus making them do everything we can do. Imagine a robot doing yoga, karate, even a ballet all by itself without the rusty old controls and commands. Researchers have come with all kinds of robots and best of all social robots for social interaction so we have come with all kinds of robots what’s next? Robot scientists and researchers! Why not? It is highly evident that robot can think in new dimensions to solve issues.
Women in Automation - Intro to Studio Session 1Cristina Vidu
With this very first product training session we kick off the RPA Developer thread of the program and get you started with UiPath Studio in a completely assisted and supportive manner by our very own UiPath MVPs. From women in RPA to all the women who wish to step into the automation world.
🌺 About this event:
What is RPA (explain RPA technology)
Why RPA (explain technological benefits)
Why RPA as a career
Platform overview
Small automation demo
Install Studio demo
Q&A
Gather your courage and curiosity, and join us on March 9th!! 👩🏽🤝👩🏼
👩🏫 Your UiPath MVP trainers:
Maria Irimias, UiPath MVP, Service Delivery Manager, accesa.eu (Romania)
Nadia Ghoufa, UiPath MVP, RPA Tech Lead, Talan (France)
Background: Introduction to Augmented Reality
Projection-based Augmented Reality
Ongoing Research of the Speaker
Ending remarks: Further Research & Future Path
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Robotics Development with MATLAB - Jose Avendano 2020.06.03 | RoboCup@Home Ed...robocupathomeedu
RoboCup@Home Education
Online Classroom: Invited Lecture Series
= Robotics Development with MATLAB =
Speaker: Jose Avendano | MathWorks
Date and Time:
- June 03, 2020 (Wed) 19:00~21:00 (GMT+8 China/Malaysia)
- June 03, 2020 (Wed) 07:00~09:00 (EDT New York)
- June 03, 2020 (Wed) 13:00~15:00 (CEST Italy/France)
https://www.robocupathomeedu.org/learn/online-classroom/invited-lecture-series
Digital transformation; or how I learnt to stop worrying and love the bots!Sayan Ghosh
AI, Cognitive technologies, and RPA is on fire in the marketplace with every organisation trialling them in some shape and form. On the other hand, digital transformation is well and truly underway globally and in New Zealand supporting key business goals and aspirational roadmaps – and creating a well thought-out operating model and governance structures in organisations to deliver measurable benefits. However, while there are quite a few advocates for embedding AI / RPA / Cognitive as part of digital transformation initiatives, quite often we see them executed in a disjoint manner during delivery. In this highly interactive session, we will explore the value that a joined-up approach to AI / RPA / Cognitive may bring to your digital transformation agenda and a holistic view of business optimisation, operational agility, and customer experience. We will talk about real-life examples and learnings – and put that in perspective of reports / artefacts from analyst firms and vendors.
SDLC, DevOps, and a lifecycle approach to RPA – Lifecycle does not apply to RPA programmes alone, but the entire IT portfolio. The enterprise IT juggernaut is always moving with each bit and piece evolving in its own microcosm, while the enterprise architecture is ever changing. RPA projects need to have a coping strategy with ever changing enterprise and cloud applications and AI to ensure virtual workers have the smarts to handle these changes, leveraging automation itself. We will explore architectural principles and practices that offer robust capabilities to RPA programmes.
Vehicle for embedding Cognitive – Cognitive technologies provide great advantages in processing and handling semi-structured data. Traditional RPA provides great results in handling processes dealing with structured data – which covers about 20% of an enterprise’s data estate. However, RPA does provide a great opportunity to embed cognitive capabilities at the point of decision making, therefore freeing up human workers from repetitive tasks around unstructured data – be it running identity documents through OCR and facial recognition to validate customer identity and speed up KYC, monitoring CCTV footage, orchestrating regulatory processes such as GDPR, or integrating with eDiscovery tools to assist legal departments. We will explore reference architectures to enable and scale such use cases
Process identification and pipeline – arguably, the most critical piece of a successful RPA programme is a robust process pipeline. While enterprises hold a great deal of knowledge on their processes and pain areas, in an increasingly data driven world, what role does data and analytics play in identifying candidates for process automation based on hard data and process telemetry? Enter process mining, a relatively new discipline in the automation world that may provide significant value to larger RPA programmes.
Women in Automation - Intro to Studio Session 1Cristina Vidu
With this very first product training session we kick off the RPA Developer thread of the program and get you started with UiPath Studio in a completely assisted and supportive manner by our very own UiPath MVPs. From women in RPA to all the women who wish to step into the automation world.
🌺 About this event:
What is RPA (explain RPA technology)
Why RPA (explain technological benefits)
Why RPA as a career
Platform overview
Small automation demo
Install Studio demo
Q&A
Gather your courage and curiosity, and join us on March 9th!! 👩🏽🤝👩🏼
👩🏫 Your UiPath MVP trainers:
Maria Irimias, UiPath MVP, Service Delivery Manager, accesa.eu (Romania)
Nadia Ghoufa, UiPath MVP, RPA Tech Lead, Talan (France)
Background: Introduction to Augmented Reality
Projection-based Augmented Reality
Ongoing Research of the Speaker
Ending remarks: Further Research & Future Path
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Robotics Development with MATLAB - Jose Avendano 2020.06.03 | RoboCup@Home Ed...robocupathomeedu
RoboCup@Home Education
Online Classroom: Invited Lecture Series
= Robotics Development with MATLAB =
Speaker: Jose Avendano | MathWorks
Date and Time:
- June 03, 2020 (Wed) 19:00~21:00 (GMT+8 China/Malaysia)
- June 03, 2020 (Wed) 07:00~09:00 (EDT New York)
- June 03, 2020 (Wed) 13:00~15:00 (CEST Italy/France)
https://www.robocupathomeedu.org/learn/online-classroom/invited-lecture-series
Digital transformation; or how I learnt to stop worrying and love the bots!Sayan Ghosh
AI, Cognitive technologies, and RPA is on fire in the marketplace with every organisation trialling them in some shape and form. On the other hand, digital transformation is well and truly underway globally and in New Zealand supporting key business goals and aspirational roadmaps – and creating a well thought-out operating model and governance structures in organisations to deliver measurable benefits. However, while there are quite a few advocates for embedding AI / RPA / Cognitive as part of digital transformation initiatives, quite often we see them executed in a disjoint manner during delivery. In this highly interactive session, we will explore the value that a joined-up approach to AI / RPA / Cognitive may bring to your digital transformation agenda and a holistic view of business optimisation, operational agility, and customer experience. We will talk about real-life examples and learnings – and put that in perspective of reports / artefacts from analyst firms and vendors.
SDLC, DevOps, and a lifecycle approach to RPA – Lifecycle does not apply to RPA programmes alone, but the entire IT portfolio. The enterprise IT juggernaut is always moving with each bit and piece evolving in its own microcosm, while the enterprise architecture is ever changing. RPA projects need to have a coping strategy with ever changing enterprise and cloud applications and AI to ensure virtual workers have the smarts to handle these changes, leveraging automation itself. We will explore architectural principles and practices that offer robust capabilities to RPA programmes.
Vehicle for embedding Cognitive – Cognitive technologies provide great advantages in processing and handling semi-structured data. Traditional RPA provides great results in handling processes dealing with structured data – which covers about 20% of an enterprise’s data estate. However, RPA does provide a great opportunity to embed cognitive capabilities at the point of decision making, therefore freeing up human workers from repetitive tasks around unstructured data – be it running identity documents through OCR and facial recognition to validate customer identity and speed up KYC, monitoring CCTV footage, orchestrating regulatory processes such as GDPR, or integrating with eDiscovery tools to assist legal departments. We will explore reference architectures to enable and scale such use cases
Process identification and pipeline – arguably, the most critical piece of a successful RPA programme is a robust process pipeline. While enterprises hold a great deal of knowledge on their processes and pain areas, in an increasingly data driven world, what role does data and analytics play in identifying candidates for process automation based on hard data and process telemetry? Enter process mining, a relatively new discipline in the automation world that may provide significant value to larger RPA programmes.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
6th International Conference on Machine Learning & Applications (CMLA 2024)
RoboCup@HomeEDU AI-Focused Robotics Education by Home Service Robot DIY | Victoria October 15, 2019
1. Service Robotics R&D
RoboCup@Home EDUCATION
AI-Focused Robotics Education by Home Service Robot DIY
Victoria University 2019-10-15 | Jeffrey Too Chuan TAN
2. Jeffrey Too Chuan TAN(陈图川)
[ Education Background ]
2007 - 2010 The University of Tokyo (Japan), Department of Precision Engineering, Doctor of Engineering
2004 - 2007 Universiti Tenaga Nasional (Malaysia), Master of Mechanical Engineering
1999 - 2003 Universiti Tenaga Nasional (Malaysia), Bachelor of Mechanical Engineering (Hons.)
[ Working Experience ]
2017 - Present Associate Professor, Nankai University (China)《天津市青年千人计划》
2017 - Present Research Fellow, Tamagawa University (Japan)
2014 - 2017 Project Assistant Professor, Institute of Industrial Science, The University of Tokyo (Japan)
2015 - 2017 Adjunct Lecturer, Tokyo City University (Japan)
2013 - 2014 Project Researcher, Institute of Industrial Science, The University of Tokyo (Japan)
2011 - 2013 Project Researcher, National Institute of Informatics (Japan)
2010 - 2011 Project Researcher, Graduate School of Engineering, The University of Tokyo (Japan)
2004 - 2007 Tutor, Universiti Tenaga Nasional (Malaysia)
[ Professional Services ]
2016 - Present Committee (Service and Junior), World Robot Summit
2016 - Present Organizing Committee, RoboCup Federation (@Home)
2015 - Present Committee, RoboCup@Home Education
2014 - Present Organizing Committee, RoboCup Japan (@Home)
2
Profile
3. RoboCup@Home
RoboCup@Home aims to foster the development of service and assistive robot technology to make possible
future personal domestic applications. The competitions comprise of a set of benchmark tests to evaluate the
robots’ capabilities in realistic home environment settings and scenarios, with the research focuses on: human-
robot interaction and cooperation, navigation in dynamic environments, computer vision and object recognition
under natural light conditions, object manipulation, adaptive behaviors and learning, ambient intelligence, and
system integration.
3
4. Outline
1. Service Robotics R&D
2. Prologue: Team KameRider
3. RoboCup@Home EDUCATION Initiative
a. Education Challenge
b. Educational Open Robot Platforms
c. Outreach Programs
6. Home Service Robot DIY
a. Robot LEG
– Autonomous Navigation
b. Robot EYES
– Visual Perception
c. Robot ARM
– Object Manipulation
d. Robot MOUTH
– Human-Robot Interaction
e. Robot BRAIN
– AI, Machine Learning,
Cloud Computing, Big Data
6
8. Robot EYES – Visual Perception
• Image Processing by OpenCV
• Deep Learning Object Detection by YOLO
8
9. Robot EYES – Visual Perception
• Person recognition result in RoboCup 2016
9
10. Improving Deep Learning Based Object Detection by
CycleGAN Method Under Inconsistent Illumination Conditions
10
Three illumination conditions of
the real environment
CycleGAN is used to realize the mutual
transformation of scenes
Dark environment before
brightness enhancement
Dark environment after
brightness enhancement
Object detection after
brightness enhancement
The top view of the
visual task scene and
the robot vision with
supplementary light
Object detection
confidence level
improvement
11. Robot LEG – Autonomous Navigation
11
• Indoor Autonomous Navigation
– Adaptive Monte Carlo Localization (AMCL)
– Simultaneous Localization and Mapping (SLAM)
– Static and Dynamic Obstacle Avoidance
12. Robot ARM – Object Manipulation
12
http://wiki.ros.org/turtlebot_block_manipulation
Object Manipulation
13. Multi-Object Grasp Planning in High Distribution Density
using Inverse Reachability Map and Base Repositioning
13
Experiment environment and object distribution IRM of different type of objects
System components and operation flow
Experiment results
15. Human-Robot Interaction (HRI)
HRI Research, Development and Applications
Human-Robot Collaborative Work
15
Human-robot collaborative cell production system
17. Robot BRAIN – AI, Machine Learning, Cloud Computing, Big Data
17
Client Systems
Robot Learning
Knowledge
Transfer
Cloud System
• Processing Servers
• Databases
?
18. Robot BRAIN – AI, Machine Learning, Cloud Computing, Big Data
• Crowdsourcing of virtual HRI for collaborative strategy learning
18[J. T. C. Tan, Y. Hagiwara, T. Inamura, “Robot Learning Framework via Crowdsourcing of Human-Robot Interaction
for Collaborative Strategy Learning,” in Proc. of the 24th IEEE RO-MAN (Interactive Session), IS04, 2015]
19. State parameters:
• Self
• Action
• Object(Target)
• Location
𝑆𝑒𝑙𝑓_𝐴𝑐𝑡𝑖𝑜𝑛𝑖 = 𝑓 𝑆𝑒𝑙𝑓_𝐴𝑐𝑡𝑖𝑜𝑛𝑖−1, 𝑃𝑎𝑟𝑡𝑛𝑒𝑟_𝐴𝑐𝑡𝑖𝑜𝑛𝑖, 𝑊𝑜𝑟𝑘_𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑖
𝐴𝑔𝑒𝑛𝑡_𝐴𝑐𝑡𝑖𝑜𝑛(𝑂𝑏𝑗𝑒𝑐𝑡, 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛)
𝑊𝑜𝑟𝑘_𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 = 𝑂𝑏𝑗𝑒𝑐𝑡1(𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛), … , 𝑂𝑏𝑗𝑒𝑐𝑡 𝑛(𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛)
“Minimum information” to describe the current state
Collaborative Intelligence
19
• Partner
• Action
• Object(Target)
• Location
• Work
• Action(Static)
• Object1-n
• Location1-n
• Condition1-n(Omitted)
20. Extraction of Embodied Collaborative
Behaviors from Cyber-Physical HRI with
Immersive User Interfaces
• Contents
– (See) Visual Observation
• Movement of HMD to
determine observed target
– (Say) Verbal Communication
• Spoken speech
– (Do) Action
• Agent’s body movement to
determine traveled path
• Timing
– Contents’ occurrence timings
w.r.t. collaboration operation
22. Robot BRAIN – AI, Machine Learning, Cloud Computing, Big Data
• Crowdsourcing of virtual HRI for collaborative strategy learning
22
23. Robot BRAIN – AI, Machine Learning, Cloud Computing, Big Data
• Crowdsourcing of virtual HRI for collaborative strategy learning
23
Handyman (GPSR) Interactive Clean Up
Human Navigation
31. 2013 The Beginning of Team KameRider
2013.05.03-06 RoboCup Japan Open 2013 Tokyo, Japan
• [UT] Jeffrey
• [Award] JSAI Award [SIGVerse for RoboCup @Home Simulation]
• [Award] RoboCup @Home Simulation [2nd Place]
2013.06.24-07.01 RoboCup 2013 Eindhoven, Netherlands
(International)
• [Symposium] Poster: “Open Web Based Development Platform for
RoboCup @Home Simulation”
• [Symposium] Oral: “Development of RoboCup@Home Simulation
towards Long-term Large Scale HRI”
32. 2014 Forming Collaboration
UT-NKU (China), UT-UTM (Malaysia)
2014.03-06 Internship of Mr. Tey @ SIT, Japan
• [Internship] Mr. Tey (UTM) assisted Jeffrey's team in the
development of a basic robot platform for RoboCup
@Home
2014.05.03-06 RoboCup Japan Open 2014 Fukuoka, Japan
• [UT] Jeffrey, [NKU] 6 members, [UTM] Tey Wei Kang
• [Award] JSAI Award [Standard Platform for RoboCup
@Home]
• [Award] RoboCup @Home Simulation [2nd Place]
33. 2014 Forming Collaboration
UT-NKU (China), UT-UTM (Malaysia)
2014.06-09 Internship of Mr. Seow @ UT, Japan
• [Internship] Mr. Seow (UTM) develops the basic robot
platform for RoboCup @Home based on the RCF support
2014.12.06 Intelligent Home Robotics Challenge 2014, Tokyo
• [UT] Jeffrey, [UTM] Lim Kian Sheng, Mohamad Hafizuddin
bin Majek, Muhammad Faiz bin Muhammad Rozi
• [Award] Mobile Robot Category 3rd Place
• [Award] Overall 3rd Place
34. 2015 Starting Education Challenge
2015.05.03-06 RoboCup Japan Open 2015 Fukui, Japan
• [UT] Jeffrey, [NKU] 3 members, [UTM] Muhammad
Najib Abdullah, Nicole Tham Lei May
• [Award] RoboCup @Home SPL (Beta) [1st Place]
• [Award] RoboCup @Home Simulation [3rd Place]
35. 2015 Entering International RoboCup
2015.07.17-23 RoboCup 2015 Hefei, China
(International)
• [UT] Jeffrey, [NKU] 7 members, [UTM]
Yeong Che Fai, Seow Yip Loon, Nicole Tham
Lei May
• Overall ranked 7th out of 17 qualified teams
• Top 9 teams to enter Stage 2
36. 2016 Collaborative Team UT-NKU-UTM-SIT
2016.03.24-27 RoboCup Japan Open 2016 Aichi,
Japan
• [Award] RoboCup @Home Education [2nd Place]
• [Award] RoboCup @Home Simulation [1st Place]
2016.06.30-07.04 RoboCup 2015 Leipzig, Germany
(International)
• Overall ranked 7th out of 23 qualified teams
36
37. 2017 Collaborative Team NKU-UTM-SIT
RoboCup Japan Open 2017 Nagoya
• [Award] RoboCup @Home Education [1st Place]
• [Award] RoboCup @Home Simulation [2nd Place]
RoboCup 2017 Nagoya (International)
• [Award] RoboCup @Home SSPL [Overall ranked 4th
out of 7 qualified teams]
RoboCup Asia-Pacific 2017 Bangkok
• [Award] RoboCup @Home [1st Place]
• [Award] RoboCup @Home Education [1st Place]
37
41. AI-Focused Robotics Education by
Home Service Robot DIY
The “Bridging Problem”
School-level Robotics Education vs University-level Robotics Research
• Bottom-up vs Top-down
• Conceptual Problems vs Real World Problems
The Blooming of AI, Cloud and Big Data
• Learning Platform and Ecosystem
41
42. RoboCup@Home EDUCATION
RoboCup@Home EDUCATION is an educational initiative that
promotes educational efforts to boost RoboCup@Home
participation and service robot development.
Under this initiative, currently there are 3 projects in
operation:
1. RoboCup@Home Education Challenge
2. Support the Development of Educational Open Robot
Platforms for RoboCup@Home (service robotics)
3. Outreach Programs (domestic workshops, international
academic exchange programs, etc.)
http://www.robocupathomeedu.org/
https://www.facebook.com/robocupathomeedu/
42
46. RoboCup@Home Education Challenge
• RoboCup@Home (Main)
– Since 2006
• RoboCup@Home Education Challenge
– RoboCup Japan Open 2015, Fukui (SPL Beta), Japan
– RoboCup Japan Open 2016, Aichi, Japan
– RoboCup Japan Open 2017, Nagoya, Japan
– RoboCup Asia-Pacific 2017 Bangkok, Thailand
– RoboCup Japan Open 2018, Ogaki, Japan
– European RoboCupJunior Championship (EURCJ) 2018, Montesilvano,Italy
– RoboCup 2018 Montreal, Italy
– RoboCup China Open 2019, Shaoxing, China
– European RoboCupJunior Championship (EURCJ) 2019, Trieste, Italy
– RoboCup 2019 Sydney, Australia
– RoboCup Japan Open 2019 Nagaoka, Japan (August)
• Upcoming events
– RoboCup Junior Australia Open 2019 Melbourne, Australia (October)
– RoboCup Asia-Pacific 2019 Moscow, Russia (November)
– RoboCup Japan Open 2020 Nagoya, Japan (March)
– RoboCup 2020 Bordeaux, France (July) 46
48. RoboCup@Home Education
RoboCup Japan Open 2015, Fukui (SPL Beta)
• Date: 2015 May 1 (Fri) - 4 (Mon)
• Participated Teams
1. AHP-1 eR@sers (Tamagawa University)
2. AHP-2 OIT Kitayama (Osaka Institute of Technology)
3. AHP-3 KameRider (The University of Tokyo, Nankai
University (China), Universiti Teknologi Malaysia
(Malaysia))
4. AHP-4 SOBITS (Soka University)
5. AHP-5 D.K.T. IcARus (Kanagawa Institute of
Technology)
6. AHP-6 TanichuCluster (Ritsumeikan University)
Ranking
No.
Team
Basic
Functionalities
Restaurant
Sub-Total(5/2)
FollowMe
Sub-Total(5/3)
5/2+5/3
Normalization
Technical
Challenge
(InternalJudges)
Total
1st AHP-3 KameRider 400 750 1150 300 300 1450 100 40 46 38 91.33
2nd AHP-6 TanichuCluster 150 250 400 50 50 450 31 33 41 40 53.52
3rd AHP-1 eR@sers 150 0 150 560 560 710 49 18 35 16 47.48
4th AHP-2 OIT Kitayama 400 0 400 250 250 650 45 19 30 12 42.75
5th AHP-5 D.K.T. IcARus 0 0 0 181 181 181 12 23 26 34 33.91
6th AHP-4 SOBITS 0 0 0 221 221 221 15 15 31 20 29.62
49. RoboCup@Home Education
RoboCup Japan Open 2015, Fukui (SPL Beta)
The RoboCup@Home rulebook of 2014 is based and 4 tests are selected as follows:
1. Basic Functionalities
• The description in section 5.2 Basic Functionalities (pg. 40-42) is referred.
• In section 5.2.1, 1. Pick and Place (pg. 40), the objects for the robot to pick up will
be located within the reach of the working envelope of the robot arm.
2. Follow Me
• The description in section 5.3 Follow Me (pg. 43-47) is referred.
• No change is made on the rules.
3. Restaurant
• The description in section 6.3 Restaurant (pg. 64-66) is referred.
• In section 6.3.2, 1. Guide phase (pg. 64) is omitted. The object and delivery
locations will be informed before the game.
• In section 6.3.2, 2. Navigation and manipulation phase (pg. 64), the objects for the
robot to retrieve will be located within the reach of the working envelope of the
robot arm.
4. Open Challenge
• The description in section 5.5 Open Challenge (pg. 52-54) is referred.
• No change is made on the rules.
[http://www.robocupathomeedu.org/challenges/robocup-home-education-challenge-2015/rules-2015]
50. RoboCup@Home Education
RoboCup Japan Open 2016, Aichi
• Date:
– Competition days: 2016 March 25 (Fri) - 27 (Sun)
– Team setup: 2016 March 24 (Thu)
• Venue:
– Aichi Institute of Technology, Aichi, Japan
• Participating Teams:
1. eR@sers (Tamagawa University)
2. OIT Kitayama (Osaka Institute of Technology)
3. KameRider (The University of Tokyo, Nankai
University (China), Universiti Teknologi
Malaysia (Malaysia), Shibaura Institute of
Technology)
4. SOBITS (Soka University)
5. WinKIT@DKT (Kanagawa Institute of
Technology)
6. TanichuCluster (Ritsumeikan University)
7. MMR (Meijo University)
8. ODENS (Osaka Electro-Communication
University)
9. Eruca (Tokyo City University)
51. RoboCup@Home Education
RoboCup Japan Open 2016, Aichi
The RoboCup@Home rulebook of 2015 is based and 4 tests are selected as follows:
1. Navigation Test
• The description in section 5.3 Navigation Test (pg. 50-53) is referred.
• No change is made on the rules.
2. Speech Recognition & Audio Detection Test
• The description in section 5.6 Speech Recognition & Audio Detection Test (pg. 59-
61) is referred.
• No change is made on the rules.
3. Restaurant
• The description in section 6.3 Restaurant (pg. 66-70) is referred.
• In section 6.3.3, 6. Delivering phase (pg. 67), the objects for the robot to retrieve
will be located within the reach of the working envelope of the robot arm (see
below).
4. Finals
• The description in chapter 7 Finals (pg. 79-80) is referred.
• No change is made on the rules.
[http://www.robocupathomeedu.org/challenges/robocup-home-education-challenge-2016/rules-2016]
65. Approach
• Open source platform
for service robot
– Startup base, cost
effective and community
support
• Current design:
– Basic robot platform
– Modular add-ons
66. Specifications
• Mobile Base
– TurtleBot2 (Kobuki)
• Perception Systems
– Kinect for Xbox 360
• Robot Arm
– TurtleBot Arm
– Elevated Platform
• User Interface
– Digital I/O
– Android interface
– Iconic robot facial expression system
• Software framework
– Navigation
– Manipulation
– Voice Interaction
– People/object recognition
68. Open Source Solution
Open robot platform for service robotics
• Open courseware
– http://www.robocupathomeedu.org/learn
– http://robotforall.org/opencourseware/
• Support wiki
– http://robotforall.org/wiki/
• Source codes
– https://github.com/robocupathomeedu/
• Demo videos
– https://www.youtube.com/user/kameriderteam
68
69. Hardware Cost
• Current hardware cost of Open Robot Platform:
69
Item Qty Cost (USD)
Mobile platform (TutleBot2) 1 1,000
Robot arm 1 600
Elevated upper platform 1 600
Motion sensor (MS Kinect) 2 500
Electronics and miscellaneous 1 300
Controller and interface system
(Laptop PC)
1 2,000
Total 5,000
PR2: ~400,000 USD
ORP: ~5,000 USD
81. International Academic
Exchange Programs
• 2017.01.09-18 SAKURA Science Program @ Japan
– Host: Tamagawa University (Japan)
– Visitor: 10 students and 1 staff from Kasetsart University
(Thailand)
• 2016.12-2017.03 RoboCup Internship @ Japan
– Host: The University of Tokyo (Japan)
– Intern: 1 student from Univerisiti Teknologi Malaysia
(Malaysia)
• 2016.02.26-03.06 SAKURA Science Program @ Japan
– Host: The University of Tokyo (Japan)
– Visitor: 10 students and 1 staff from Nankai University (China)
• 2016.02.03-19 SAKURA Science Program @ Japan
– Host: Shibaura Institute of Technology (Japan)
– Visitor: 10 students and 2 staff from Universiti Teknologi
Malaysia (Malaysia)
• 2014.12.06 Intelligent Home Robotics Challenge 2014
@ Japan
– Venue: Tokyo
– Participated the challenge and workshop by 3 students from
Univerisiti Teknologi Malaysia (Malaysia)
• 2014.06-09 RoboCup Internship @ Japan
– Host: The University of Tokyo (Japan)
– Intern: 1 student from Univerisiti Teknologi Malaysia
(Malaysia)
• 2014.03-06 Robotics Internship @ Japan
– Host: Shibaura Institute of Technology (Japan)
– Intern: 1 student from Univerisiti Teknologi Malaysia
(Malaysia)
82. Student Development PhD Scholarship at
Australian National University
Internship in Japan Internship in ItalyInternship in Italy
83. Next Step
• Worldwide Initiative
– RoboCup@Home
Education Community
(Challenge, Workshop)
– USA, Europe (Italy),
Thailand, China, Iran,
Malaysia, Singapore, etc.
83
• Collaboration with RoboCup Junior
• Collaboration with Industrial Partners
– MathWorks, NVIDIA, ROBOTIS
• Open Courseware and Open Robot (Hardware/Software)
Development
84. Bridging Robotics Education between High School and
University: An Outreach Development in Southeast Asia
Jeffrey Too Chuan Tan1, Kanjanapan Sukvichai2, Zool Hilmi Ismail3, Ban Hoe Kwan4,
Danny Wee Kiat Ng4, Hafiz Rashidi Harun5, Amy Eguchi6 and Luca Iocchi7
MOTIVATION – There is a big gap of missing advanced skill
sets between high school and university level of robotics
education due to the differences in bottom-up and top-
down learning approaches.
SOLUTION – We aim to initiate a bridging education layer
that abstracts advanced university level robotics
development into a learning platform suitable for high
school students. The students learn by building practical
robots and competing their robots with peers.
PROJECT – We are developing a set of hardware and
software solutions as the learning platform (Fig. 1), and
organizing a series of educational activities in the form of
workshop and competition (Fig. 2). The objective of this
work is to outreach and evaluate this effort in developing
countries in Southeast Asia.
Regional Collaborators
1. Nankai University, China
2. Kasetsart University, Thailand
3. Universiti Teknology Malaysia, Malaysia
4. Universiti Tunku Abdul Rahman, Malaysia
5. Universiti Putra Malaysia, Malaysia
6. Bloomfield College, USA
7. Sapienza University of Rome, Italy
Fig. 1 Affordable robot platforms TurtleBot2 and MARRtino
Fig. 2 Outreach programs including workshop and competition
activities in China, Japan, USA and Italy (clockwise from top left)
85. World Robot Summit – Junior Category
School Robot Challenge Workshop & Trial 2017
85
86. World Robot Summit – Junior Category
School Robot Challenge Workshop & Trial 2018
86
87. Take-Home Messages
1. Service Robotics R&D
“Everyone can learn AI and Robotics!”
2. Prologue: Team KameRider
“It works!”
3. RoboCup@Home EDUCATION Initiative
a. Education Challenge
“Let’s organize together at your region!”
b. Educational Open Robot Platforms
“Give everyone a robot!”
c. Outreach Programs
“Bring us to your community!”