[robotics-worldwide] [journals] Call for papers: Special Issue on Bio-inspired Social Robot Learning in Home Scenarios

Francisco Cruz cruz at informatik.uni-hamburg.de
Mon Oct 31 08:30:05 PDT 2016


IEEE Transactions on Cognitive and Developmental Systems
Special Issue on Bio-inspired Social Robot Learning in Home Scenarios

https://urldefense.proofpoint.com/v2/url?u=http-3A__www.informatik.uni-2Dhamburg.de_wtm_SocialRobotsWorkshop2016_CFP-5FTCDS-5FSI-5FSocialRobots.pdf&d=DgIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=GS4reqQfubXjAaNud8zhSoPPqyf4KSL8XnhZA-ezYTs&s=6CxZe5st8MuPOFtAu0KCGTxSmx7P2oFPZpJUvoVpRfk&e= 

*Call for papers*
There has been considerable progress in robotics in the last years
allowing robots to successfully contribute to our society. We can find
them from industrial environments, where they are nowadays established,
to domestic places, where their presence is steadily rising. The
proposed special issue intends to explore the following question: “How
well prepared are learning robots to be social actors in daily-life home
environments in the near future”.

The special issue is therefore not only an opportunity to address this
focuses on the latest scientific contributions on bio-inspired learning
and social robotics, but also links them with a clear focus to push the
presence of robots in people’s daily-life environment. Thus, one main
goal of the special issue is offering a common foundation for
roboticists from different fields of expertise to contribute beyond the
current state-of-the-art of learning methods in robotics especially
applied to home scenarios and recent developments in assistive robots.

The subjects of the special issue include, but are not limited to:
- Interactive reinforcement learning.
- Policy and reward shaping.
- Neural learning of object affordances and contextual affordances.
- Predictive learning from sensorimotor information.
- Learning understanding of environment ambiguity.
- Learning with hierarchical and deep neural architectures.
- Bootstrapping complex action learning in robots.
- Learning supported by external trainers, by demonstration and imitation.
- Parental scaffolding as a bootstrapping method for learning.

*Submissions*
The special issue is open for all submissions which will be
independently peer-reviewed in accordance with IEEE policy. Manuscripts
should be prepared according to the “Information for Authors” of the
journal, found at https://urldefense.proofpoint.com/v2/url?u=http-3A__cis.ieee.org_publications.html&d=DgIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=GS4reqQfubXjAaNud8zhSoPPqyf4KSL8XnhZA-ezYTs&s=9zsxhKLJesZIQeKGJSPNr0jrXsRfShLBuR42d_vGsps&e= , and submitted
through the IEEE TCDS Manuscript center under the category: "SI: Social
Robots": https://urldefense.proofpoint.com/v2/url?u=https-3A__mc.manuscriptcentral.com_tcds-2Dieee&d=DgIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=GS4reqQfubXjAaNud8zhSoPPqyf4KSL8XnhZA-ezYTs&s=Dbns4JLde4Y3fvrM_6w4mLaHUvHJcfZy16TGny8QlvY&e= . Papers submitted
must not have been published previously, though they may represent
significant extensions of prior work.

*Important dates*
31 January 2017 - Deadline for manuscript submission.
15 April 2017 - Notification of authors.
15 May 2017 - Deadline for revised manuscripts.
15 June 2017 - Final decisions.

For further information, please contact one of the following guest
editors in this order:

Francisco Cruz
Knowledge Technology Institute, University of Hamburg, Germany
cruz at informatik.uni-hamburg.de

Jimmy Baraglia
Emergent Robotics Laboratory, Osaka University, Japan
jimmy.baraglia at ams.eng.osaka-u.ac.jp

Yukie Nagai
Emergent Robotics Laboratory, Osaka University, Japan
yukie at ams.eng.osaka-u.ac.jp

Stefan Wermter
Knowledge Technology Institute, University of Hamburg, Germany
wermter at informatik.uni-hamburg.de


Francisco Cruz
Research Associate
Knowledge Technology Group
Department of Informatics
University of Hamburg
Vogt-Kölln-Str. 30
22527 Hamburg, Germany
Office F-217

Phone: +49 40 42883 2524
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.knowledge-2Dtechnology.info&d=DgIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=GS4reqQfubXjAaNud8zhSoPPqyf4KSL8XnhZA-ezYTs&s=rTOq_1_FKFWpDJ4P8jQeKSwykMlWJNKI09m-HRj4EyQ&e= 



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