CON-HUMO

Control based on Human Models

 Coordinatore TECHNISCHE UNIVERSITAET MUENCHEN 

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 Nazionalità Coordinatore Germany [DE]
 Totale costo 1˙494˙640 €
 EC contributo 1˙494˙640 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2013-StG
 Funding Scheme ERC-SG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-02-01   -   2019-01-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITAET MUENCHEN

 Organization address address: Arcisstrasse 21
city: MUENCHEN
postcode: 80333

contact info
Titolo: Ms.
Nome: Ulrike
Cognome: Ronchetti
Email: send email
Telefono: 498929000000
Fax: 498929000000

DE (MUENCHEN) hostInstitution 1˙494˙640.00
2    TECHNISCHE UNIVERSITAET MUENCHEN

 Organization address address: Arcisstrasse 21
city: MUENCHEN
postcode: 80333

contact info
Titolo: Prof.
Nome: Sandra
Cognome: Hirche
Email: send email
Telefono: +49 89 28925722
Fax: +49 89 28925724

DE (MUENCHEN) hostInstitution 1˙494˙640.00

Mappa


 Word cloud

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physical    smart    modeling    model    difficult    prediction    learning    machine    humo    data    con    behavior    interaction    human    fundamental   

 Obiettivo del progetto (Objective)

'CON-HUMO focuses on novel concepts for automatic control based on data-driven human models and machine learning. This enables innovative control applications that are difficult if not impossible to realize using traditional control and identification methods, in particular in the challenging area of smart human-machine interaction. In order to achieve intuitive and efficient goal-oriented interaction, anticipation is key. For control selection based on prediction a dynamic model of the human interaction behavior is required, which, however, is difficult to obtain from first principles. In order to cope with the high complexity of human behavior with unknown inputs and only sparsely available training data we propose to use machine-learning techniques for statistical modeling of the dynamics. In this new field of human interaction modeling – data-driven and machine-learned – control methods with guaranteed properties do not exist. CON-HUMO addresses this niche. Key methodological innovation and breakthrough is the merger of probabilistic learning with model-based control concepts through model confidence and prediction uncertainty. For the sake of concreteness and evaluation the focus is on one of the most challenging problem classes, namely physical human-machine interaction: Because of the physical contact between the human and the machine not only information, but also energy is exchanged posing fundamental challenges for real-time human-adaptive and safe decision making/control and requiring provable stability and performance guarantees. The developed methods are a direct enabler for societally important applications such as machine-based physical rehabilitation, mobility and manipulation aids for elderly, and collaborative human-machine production systems. With its fundamental results CON-HUMO lays the ground for the systematic control design for smart human-machine/infrastructure interaction.'

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