[Comp-neuro] CFP - Emerging Spatial Competences: From Machine Perception to, Sensorimotor Intelligence

Agostino Gibaldi agostino.gibaldi at unige.it
Sun Dec 29 22:38:09 CET 2013

Dear Researcher,

We would like to remind you the Special Issue on *"Emerging Spatial 
Competences: From Machine Perception to Sensorimotor Intelligence"*, 
organized by the PSPC Lab (www.pspc.unige.it) for the journal RAS - 
Robotics and Autonomous Systems 

The Special Issue (see below for a more detailed description) aims to 
investigate how the mutual influence between the perception of the 
environment and the interaction with it can be extended to support 
co-evolution mechanisms of perceptual and motor processes. Considering 
your expertise in the topics**we would be glad to receive a contribution 
from you. All the received contributions will be refereed by a panel of 
experts according to the policies of the RASjournal.

*We use the occasion to wish you a Merry Christmas and a Happy New Year!!!*


Agostino Gibaldi


*Robotics and Autonomous Systems Journal*


*Special Issue on "Emerging Spatial Competences: From Machine Perception 
to Sensorimotor Intelligence"*


*_Aims and Objectives_*

Following the recent evolution of robotics and AI in different fields of 
application, the increasing complexity of the *actions* that an 
artificial**agent needs to perform, is directly dependent on the 
complexity of the *sensory information *that it can acquire and 
*interpret*, /i.e./ *perceive*.

>From this point of view, an efficient and internal representation of the 
sensory information is at the base of a robot to develop a *human-like 
capability* of interaction with the surrounding environment. 
Particularly, in the space at a *reachable distance*, not only visual 
and auditory, but also tactile and proprioceptive information rise to be 
relevant to gain a comprehensive spatial cognition. This information, 
coming from different senses, can be in principle integrated and used to 
experience an awareness of the environment both to actively interact 
with it, and to calibrate the interaction itself. Besides, the early 
sensory and sensorimotor mechanisms, that at a first glance may appear 
simple processes, are grounded on highly structured and complex 
algorithms that are far from being understood and modeled. By exploiting 
an early synergy between *sensing modules*and *motor control*, the loop 
between action and perception comes to be not just closed at system 
level, but shortened at an inner one. This would allow not only the 
emergence of *spatial competences* but also their *continuous 
adaptation*to changes in the environment or in the body, which could 
modify its interactions with the world.

The aim of this special issue is to survey a state of the art of 
methodologies, concepts, algorithms and techniques that would serve as 
bricks on which to build and develop artificial agents with such a 
spatial competence; perceptual and cognitive understanding of space 
should emerge from sensorimotor exercise.

The *action-perception loop* has never been so close!

Paper Submission_*

We invite original contributions that provide novel solutions to address 
the relevant topics including but not limited to:


    -Theoretical or practical aspects of machine sensing (for computer
    vision, robot audition, artificial touch, etc.)

    -Multisensory data fusion, processing, learning and integration

    -Computational neural modeling

    -Embodied robotics: perception, cognition, and behaviors

    -Machine learning for sensorimotor control and intelligence

    -Neural networks: models, theories, learning algorithms and applications

    -Engineering application of sensorimotor intelligence to pattern
    recognition, computer vision, speech recognition, human-robot

As a follow-up of the IJCNN 2013 special session, we invite in 
particular the special session participants to submit profoundly 
extended versions of their conference submission to go through a new 
peer review process, together with contributions not published in the 
conference proceedings. **

Papers should be typeset according to the format instructions for the 
Robotics and Autonomous Systems Journal, available on the Elsevier web 

*_Important Dates_*

§January 31, 2014: Paper submission deadline

§March 31, 2014: Notification of paper acceptance

§April 30, 2014: Camera ready paper submission

§Late Spring 2014: Expected publication date

*_Guest editors_*

*Agostino Gibaldi*, agostino.gibaldi at unige.it

Department of Informatics, Bioengineering, Robotics and System Engineering

University of Genoa, Italy

Advanced ResearchCenteron Electronic Systems (ARCES)

University of Bologna, Italy

/Agostino Gibaldi received his degree in Biomedical Engineering from the 
University of Genoa, Italy, in 2007, and his Ph.D. in 2011. Since the 
master thesis he is with the Physical Structure of Perception and 
Computation (PSPC) Group where he is actually a post doc. Recently, he 
joined the Computer Vision Group of the //Advanced Research Center on 
Electronic Systems (ARCES), working on data analysis computer aided 
diagnosis for CT perfusion related to tumour lesions. //His research 
interests are related to cortical models of V1, MT and MST areas, in 
relation with the estimation of disparity, the control of vergence eye 
movements, and the optic flow analysis for navigation, for their 
real-time implementation on robot platforms so to obtain active 
behaviours and adaptation to the environment. Aside, he also worked on 
neural networks and learning, eye tracking algorithms, camera 
calibration, 3D data modelling for virtual reality, CT perfusion and 
image registration./


*Silvio P. Sabatini* <http://www.pspc.unige.it/>, silvio.sabatini at unige.it

Department of Informatics, Bioengineering, Robotics and System Engineering

University of Genoa, Italy


/Silvio P. Sabatini received the Laurea Degree in Electronics 
Engineering and the Ph.D. in Computer Science from the //University//of 
//Genoa//in 1992 and 1996. He is currently Associate Professor of 
Bioengineering at the Department of Informatics, Bioengineering, 
Robotics and System Engineering of the //University//of //Genoa//. In 
1995 he promoted the creation of the "Physical Structure of Perception 
and Computation" (PSPC) Lab to develop models that capture the 
"physicalist" nature of the information processing occurring in the 
visual cortex, to understand the signal processing strategies adopted by 
the brain, and to build novel algorithms and architectures for 
artificial perception machines. His research interests relate to visual 
coding and multidimensional signal representation, early-cognitive 
models for visually-guided behavior, and robot vision. He is author of 
more than 100 papers in peer-reviewed journals, book chapters and 
international conference proceedings./


*Sylvain Argentieri* <http://people.isir.upmc.fr/argentieri>, 
sylvain.argentieri at upmc.fr

Institute for Intelligent Systems and Robotics (ISIR)

Université Pierre et Marie Curie, Paris, France


/Sylvain Argentieri received his Master's degrees in Robotics from the 
Pierre et Marie Curie University, Paris, and in Electronics from Ecole 
Normale Supérieure, Cachan, France, in 2003. He then received his Ph.D. 
in Computer Science from the //Paul////Sabatier////University//, 
//Toulouse//, //France//, in 2006. After two years as an Assistant 
Professor at LAAS-CNRS (Laboratory for Analysis and Architecture of 
Systems) in the same University, he is now Associate Professor at the 
"Active Multimodal Perception" group in the Institute for Intelligent 
Systems and Robotics of the //Pierre//et 
//Marie////Curie////University//since 2008. He also obtained in 2002 the 
highest teaching diploma in //France//(Agrégation externe) in 
Electronical Science. His research interests relate to artificial 
audition in a robotics context, from array processing methods to 
binaural approaches, for sound source localization, speaker recognition, 
human-robot interaction, etc. He is also interested in active approaches 
to multimodal perception and sensorimotor integration./


*Zhengping Ji*, jizhengp at gmail.com <mailto:jizhengp at gmail.com>

Advanced Image Research Laboratory (AIRL)

Samsung, Pasadena, CA, U.S.A


/Zhengping Ji received his B.S. degree in Electrical Engineering from 
Sichuan University//, //China, in 2003 and the Ph.D. in Computer Science 
from Michigan State University, USA, in 2008. From 2009 to 2010, he held 
a postdoctoral fellow position at the Center for the Neural Basis of 
Cognition, //Carnegie////Mellon////University//, working on the DARPA 
RealNose Project. After that, he spent two years in Los Alamos National 
Laboratory, where he was a Research Associate conducting researches on 
computational modelling of the brain's visual pathways. He is now a 
Senior Research Scientist at Advanced Image Research Laboratory of 
Samsung Electronics. His current research interests lie in computer 
vision, computational neuroscience and machine learning. Specifically, 
he seeks to develop a series of deep learning models to generate 
cortex-like hierarchical sparse representation for a variety of tasks in 
vision, including generic object recognition, object detection and 
segmentation, image denoising and compression, and vision-based 
autonomous navigation. He is a Vice Chair of Task Force on Bio-Inspired 
Self-Organizing Collective Systems at IEEE Computational Intelligence 
Society, and a committee member of the Brain-Mind Institute, USA./


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