This webpage outlines the project and links to more detailed information
Humanoid developmental robotics, autonomous learning, reinforcement learning, actions hierarchies, neural networks, novelty detection, developmental and cognitive psychology, brain, dopamine.
IM-CLeVeR aims to develop a new methodology for designing robots controllers that can: (1) cumulatively learn new efficient skills through autonomous development based on intrinsic motivations, and (2) reuse such skills for accomplishing multiple, complex, and externally-assigned tasks. During skill-acquisition, the robots will behave like children at play which acquire skills autonomously on the basis of “intrinsicmotivations”. During skill-exploitation, the robots will exhibit fast learning capabilities and a high versatility in solving tasks defined by external users due to their capacity of flexibly re-using, composing and readapting previously acquired skills.
This overall goal will be pursued investigating three fundamental scientific and technological issues: (1) the mechanisms of abstraction of sensory information; (2) the mechanisms underlying intrinsic motivations, e.g. “curiosity drives” that learn to focus attention and learning capabilities on “zones of proximal development”; (3) hierarchical recursive architectures which permit cumulative learning. The study of theseissues will also be fuelled by a reverse-engineering effort aiming at reproducing with bio-mimetic models theresults of empirical experiments run with monkeys, children, and human adults.
The controllers proposed will be validated with challenging demonstrators based on a single humanoid robotic platform (iCub). As a main outcome, the project will significantly advance the scientific and technological state of the art, both in terms of theory and implementations, in autonomous learning systems and robots. This overall goal will be achieved on the basis of the integrated work of a highly interdisciplinary Consortium involvingleading international neuroscientists, psychologists, roboticists and machine-learning researchers.
Project full title: Intrinsically Motivated Cumulative Learning Versatile Robots
Partners: See details here
Coordinator: Gianluca Baldassarre
Team Leaders: Marco Mirolli (CNR-ISTC-LOCEN), Elisabetta Visalberghi (CNR-ISTC-UCP), Eugenio Guglielmelli (UCBM-LBRB), Flavio Keller (UCBM-LDN), Peter Redgrave (USFD-Neurosc.), Kevin Gurney (USFD-Model.), Jochen Triesch (FIAS), Ulrich Nehmzow (UU), Mark Lee (AU), Juergen Schmidhuber (IDSIA-SUPSI)
Project Officer: Cécile Huet
Contract number: FP7-ICT-IP-231722
Funding Institution: European Commission (European Union)
Funded under: Seventh Framework Programme
Work Programme Theme 3: ICT – Information and Communication Technology
Call identifier: FP7-ICT-2007-3 Challenge 2: Cognitive Systems, Interaction, Robotics
Start: 01/01/2009 (start of scientific work: 01/05/2009)
Total duration: 52 months
Total EU Funding: 5.899.884 euros
Total Budget (EU Funding + Cofunding): 7.726.783 euros
International Scientific Advisory Board:
Juyang Weng, Christian Balkenius
Peter Dayan, Yael Niv, Paul Verschure
Computational modelling and developmental psychology
Claes von Hofsten, Linda B. Smith, Matthew Shlesinger
Machine learning, autonomous robotics, information-theory
Richard Sutton, Frederic Kaplan, Pierre-Yves Oudeyer, Max Lungarella, Daniel Polani
Acknowledgments: This research has received funds from the European Commission 7th Framework Programme (FP7/2007-2013), ''Challenge 2 - Cognitive Systems, Interaction, Robotics'', grant agreement No. ICT-IP-231722, project ''IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots''.