Distributed Intelligence For Robotic Systems

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

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Center for Intelligent Technologies www.ai-cit.sk Founded : September

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Our Primary Focus Areas Research in selected areas of AI Education – InS-Bc, AI-Ing., AI-PhD.

Application potential for AI

Intelligent Technologies

Demonstration and benchmarking for InT

Computational Intelligence and contribution to InT. 3

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

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

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Center for Intelligent Technologies, www.aicit.sk

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Remote CIT Lab concept

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Theoretical Introduction Slovakia … where Czech writer Karel Čapek and his brother Jozef invented word “ROBOT”  Trenčianske Teplice,SK, Hotel PAX

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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..)

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What is Artificial Intelligence ? Many definitions ...

HUMAN

MACHINE

Simplified view to AI : AI – takes labour from HUMAN and gives it to Machine 10. 3. 2013


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From Simply to very complex -

Remote control .. ABS, Rain Detector in the car .... .. Autonomous Systems

SUPERVISION

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Machine IQ – theory 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 and GIQ-T is constant 1 13

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 14

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

Role of CI and hybrid systems

Distributed Incremental Intelligence

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Qua Vadis Intelligent Robotics ??? Very important for Robot applications ……. many Technological challenges

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

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

Family #1

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WILKI Servers Family #2

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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 ? 10. 3. 2013


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MF ARTMAP neural network Membership Function ARTMAP 1. 2. 3. 4.

Feature space consist of data – clusters of data Clusters are considered – fuzzy sets – fuzzy clusters Fuzzy class – consist of fuzzy sets The membership of unknown object to each trained object is calculated 5. Similarities and homogenity / heterogenity of classes can be revealed 10. 3. 2013


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

𝑓 𝑥1 , 𝑥2 , … , 𝑥𝑠 =

1 2𝜋

𝑠

𝐶

1 𝑇 −1 𝑒 2 𝑥−𝜇 𝐶 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.

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MF-Artmap NN topology

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Principles of MFARTMap processing Classes i, j are trained

𝜇𝑖 (z)