Distributed Intelligence for Robotic Systems

Prof. Peter Sinčák & Mária Virčíková, MSc. Center for Intelligent Technologies Department of Cybernetics and AI,

Faculty of EE & Informatics, TU Košice, Slovak Rebublic , EU

10. 3. 2013 Center for Intelligent Technologies,

www.ai-cit.sk 1

www.ai-cit.sk

Founded : September

1995

Center for Intelligent Technologies

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Research in selected areas of AI

Application potential for AI

Computational Intelligence and

contribution to InT.

Demonstration and benchmarking for InT

Education – InS-Bc, AI-Ing., AI-PhD.

Intelligent Technologies

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Our Primary Focus Areas

Our Global Challenge

Evolving Learnable Distributed Framework for Companion

Robotic Systems

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Our Global Challenge

• Evolving & Learnable

• Distributed Framework

• Companion Robotic Systems

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Our Reserach Challenges • Teleoperation as a kick-off start towards assisted teleoperation and mission

autonomy is the important part of Intelligent Robotics, for various robots including monitoring of remote robots, standardization for various robots around the GLOBE, learn-able , incremental ,

• Make a pilot study of networked Robotic Systems able to learn in incremental way

• Human Robot Interaction towards resreach in Emotional Technologies for HCI are focused

• Improve MF ARTMAP neural network for dynamic feature space in sense of Wald sequential classification for distributed environment

• Get model of human behavior in interactive Evolutionary computation to return to Evolutionary Computation.

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Staff & equipment

• One professor

• 4 assistent professors

• 7 PhD students

• 20 MSc. Students

• 20 BSc. Students

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15 NAO Robots, 2 Stereo-heads, 2 AIBO Robots …

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Remote CIT Lab concept

Theoretical Introduction

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Slovakia … where Czech writer Karel Čapek and his brother Jozef invented word “ROBOT”

Trenčianske Teplice,SK, Hotel PAX

Our approach …

(Robot is not a Toy !!!)

Robot NAO for CIT is a research and technological tool to make envision a future Collaboration of mankind and technology and discover new possibilities of using

Robot and other technology in Era of Cloud Computing and Internet of things

(Robot Nao is a serious Guy !!! :) ) (other Robots on the way, Hanson Robotics, Qbo Robots, NordStrom, ASIMO, HRP4, ICUP,

Japaness robots, Korean robots..) 3/10/2013 10

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What is Artificial Intelligence ?

Many definitions ... Simplified view to AI :

AI – takes labour from HUMAN and gives it to Machine

HUMAN MACHINE

From Simply to very complex

- Remote control

- ..

- ABS, Rain Detector in the car ....

- ..

- Autonomous Systems

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SUPERVISION

Machine IQ – theory

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MIQ is IQ is in correlation of the „amount“

of Labor taken from Human and given to Machine during particular TASK (T) .

GIQ-T = HIQ-T + MIQ-T

HIQ-T and MIQ-T are from interval <0,1> and

GIQ-T is constant 1

MIQ-T via Autonomity

IF MIQ-T is 0 – HIQ-T must be 1 – human is doing a T

IF MIQ-T is 1 – HIQ-T must be 0 – human is only supervising

Machine is Autonomous Machine Autonomity Index : MAI-T = MIQ-T / HIQ-T MAI-T = O ??? – manual process MAI-T = infinity – Autonomous process

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Bad news ...

How to define/compute a HIQ for TASK ????

How to define/compute MIQ for particular TASK ?????

Can be determined in the selected domian –

CAR – Interaction CAR – DRVIVER

IEEE Standart committee

WCCI 2010 – discussion

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Take labor from humans „simulate“ a human behavior.... How ...????

Full-embodied Intelligence Distributed Incremental Intelligence

Role of CI and hybrid systems 16

Very important for Robot

applications …….

many Technological

challenges

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Qua Vadis Intelligent Robotics ???

Distributed Image processing

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The Fundamental approach to categorization inspiration by prof. Bezdek and Pal

Distributed AI (cloud Robotics ?) WILKI – World Incremental Learning Knowledge Integrator

Family #2

Family #1

#1 #2

#n

#1

#2 #n

WILKI Servers

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Neural networks for knowledge identification

2 main options in Supervised mode

• Pure supervised mode NN with BP – discrimination hyper-planes in feature space

(not suitable for incremental learning )

• Clustering with supervised approach – ARTlike

Clusters can be revealed

(good for incremental learning )

Problem : Feature space ? Static or Dynamic dimensions ?

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MF ARTMAP neural network

Membership Function ARTMAP

1. Feature space consist of data – clusters of data

2. Clusters are considered – fuzzy sets – fuzzy clusters

3. Fuzzy class – consist of fuzzy sets

4. The membership of unknown object to each trained object is calculated

5. Similarities and homogenity / heterogenity of classes can be revealed

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Membership function of X to class i - 𝜇𝑖(X) – fuzzy clusters

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𝑓 𝑥1, 𝑥2, … , 𝑥𝑠 =1

2𝜋 𝑠 𝐶𝑒12 𝑥−𝜇 𝑇𝐶−1 𝑥−𝜇 ,

s - is feature space dimensions , C is covariance matrix related to data in the clusters, 𝜇 is mean Value of the cluster data . We do calculate it for each fuzzy cluster and that means for each fuzzy class.

MF-Artmap NN topology

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Principles of MF-ARTMap processing

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Trained MF ARTMAP

𝜇𝑖(x) = 1

𝜇𝑗(x) << 1

𝜇𝑗(y) = 1

𝜇𝑖(y) << 1

j-trained class

i-trained class

y-input

x-input

z-input 𝜇𝑖(z) << 1

𝜇𝑗(z) << 1

Classes i, j are trained

Jeffers/Matussita distance

• The JM distance between two classes is given to be:

𝐽𝑖𝑗 = 2(1 − 𝑒−𝛼)

• where α is the Bhattacharyya distance:

𝛼 =1

8𝜇𝑖 − 𝜇𝑗

𝑇 𝐶𝑖+𝐶𝑗

2

−1𝜇𝑖 − 𝜇𝑗 +

1

2𝑙𝑛

𝐶𝑖+𝐶𝑗

2 𝐶𝑖 𝐶𝑗

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Intra and Inteclass Analysis of data

Using Jeffers Matusista distance we are able using MF ARTMAP … to get analysis of clases …

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Class house is strong homogenous with membership (0.02).

Class house is homogenous with membership (0.1).

Class house is heterogenous with membership (0.9).

Class door is a minor subclass of Class(es) house with membership (0,05).

Class door is a medium subclass of Class(es) house with membership (0,96).

Class door is a major subclass of Class(es) house with membership (0,04).

Class door is a minor subclass of Class(es) house with membership (0,05).

Class door is a medium subclass of Class(es) house with membership (0,96).

Class door is a major subclass of Class(es) house with membership (0,04).

Results of classification in distributed sense

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MASS server future

Resulst on test data

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Circle1* Circle 2*

Circle 1 97.99% 2.01%

Circle 2 1.198% 98.802%

Circle1* Circle2*

Circle1 88.31% 11.69%

Circle2 6.827% 93.173%

Quo Vadis MF-ARTMAP

We want work in the close future

1. to implement MF ARTMAP for fully distributed sense

2. To port it as software as a service for Cloud service

3. To design a feature and knowledge database on cloud

4. there are many challenges and domain oriented approach seems to be a first step…

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Teleoperation and remote diagnostics – WHY ????

• Remote monitoring of NAO joins, sensors, … video, ….. • Remote Tele - assistance – Tele – advice (autism) • Tele-operation with learning …. Mission Autonomy …

NAO learns …. Global knowledge… • Tele-operation with Integration, fusion of

sources….camera, sensors, …. (Robot ready environment – Intelligent Space )

• Remote soccer …. Virtual Remote Coach • Remote commercial center … pre-purchase testing ,

remote renting service ….on-line Nao shows … • Remote rehabilitation services....

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Importance of the problem

Our approach to the problem

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INTERFACE – middleware …

Monitoring

Experience with Middle-wares in robotics

• Re-usable components

• Interconnection with existing systems

• International standards

• RT Middleware, OPC UA, ROS

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TeleScope Remote device monitoring network

http://telescope.cherrysro.com/

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Are we ready … to have e.g. 50 000 Naos … ??? Customer service – help lines.. – …. Our system should be ready to monitor up to 3000 Naos … security is under control...

Areas of interest

• Monitoring / Diagnostic

• Pre-Sale activity

• Interface standardization

• Tele-activity ...

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Functionality

• Remote monitor & control

• For “any” device

• Web service

• Multi user control (even for one device)

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What is Important?

• Pre-sale product experience

• Simple interface

• Simple accessibility

• Device condition monitoring

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Dataflow in TeleScope

Web

Event server

Device

Event server

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Security issue

Infrastructure

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TeleScope Interface

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TeleScope - Senzors

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The Road Ahead

Working prototypes

Video stream implementation

More devices and partners

Improve visualization

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Smart Tele-operating system – STS v.1.0

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Interface….. Different sources…

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NAO…from overhead camera 1

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Task … to move … Manual control using PlayS-controler

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Action to move … path following control..

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Visual control of automatic procedure - replay

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Possible involvement of Dron image info about Nao

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What influence have distributed approach …

Technologies are changing core methods ……

recognition can be changes to powerfull searching

Will the Robot be able to think ? Will be ever have consciousness ?

Rather …. Find a question , Ontologies , self-protecting … eternal

systems , Internet of THINGS – important for ROBOTICS

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Other activities of Center for Intelligent Technologies

• Collaboration with companies in Robotic Slovakia, new companies Aldebaran, Nordstrom

• Support for interaction between ART people and Robotics Towards Robotics ART – new media ART - towards Emotional technology for Computers and Robotics for benefit of mankind

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Emotions for Human Computer/Robot Interation for Human Comfort and high performance

Some recent publications • Mária Virčíková ... [et al.] - 2012. Neural Approach for Personalised Emotional Model in

Human-Robot Interaction - In: WCCI 2012 : IEEE World Congress on Computational Intelligence : June 10-15, Brisbane, Australia. - [Brisbane] : IEEE, 2012 P. 970-977. - ISBN 978-1-4673-1489-3 Brisbane

• P. Smolar, P. Sincak, M. Virčíková : Intelligent Image Categorization Object Categorization with Artmap Neural Networks, Saarbrucken : LAP Lambert Academic Publishing - 2012. - 181 p.. - ISBN 978-3-659-24795-8.

• Mária Virčíková, Zlatko Fedor, Peter Sinčák : Design of Verbal and Non-Verbal Human-Robot Interactive System In: Humanoids 2011: 2011 11th IEEE-RAS International Conference on Humanoid Robots: 26. - 28.10.2011: Bled, Slovenia. - Bled, Slovenia : IEEE, 2011 S. 87–92. - ISBN 978-1-61284-867-9

• Stanislav Kmet - Peter Sincak - Peter Stehlik : Artificial neural network for creep behaviour predictions of a parallel-lay aramid rope under varying stresses, Strain, An International Journal for experimental Mechanics, ISSN: 1475-1305 , CC-indexed , 2010

• Marek Bundzel, and Peter Sinčák: Combining gradient and evolutionary approaches to the artificial neural networks training according to principles of support vector machines, 2006 IEEE World Congress on Computational Intelligence : international Joint Conference on Neural Networks : Vancouver, Canada, July 16 -21, 2006. - : IEEE, 2006. - - ISBN 0-7803-9489-5. - P. 4070-4076.

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Some older publications

• http://www.amazon.com/Intelligent-Technologies-Theory-Applications-Intelligence/dp/1586032569/ref=sr_1_2?ie=UTF8&s=books&qid=1275315367&sr=1-2

• http://www.amazon.com/State-Art-Computational-Intelligence-Proceedings/dp/3790813222/ref=sr_1_3?ie=UTF8&s=books&qid=1275315367&sr=1-3

• http://www.amazon.com/Machine-Intelligence-Advances-Systems-Applications/dp/981238751X/ref=sr_1_4?ie=UTF8&s=books&qid=1275315367&sr=1-4

• http://www.amazon.com/Quo-Vadis-Computational-Intelligence-Approaches/dp/3790813249/ref=sr_1_6?ie=UTF8&s=books&qid=1275315367&sr=1-6

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We would be happy if…

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We would be excited using network to Exchange experience Bussines & networking meetings, Innovation Seminars … Create a Network of International seminars using tele-conferencing We are open for video-connections and Remote lecturing and discussions

to create Invitation & Inspiraton & Invention network

Thank you for your time !!!

We are proud that Kosice, Slovakia in 2013 is European Capital of Culture…

Melina Mercury Prize

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