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SYSINT-PERROB 2014 : Perceptive Robotics Embedded Symposium - 2nd Int. Conf. on System-Integrated Intelligence

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Link: http://www.sysint-conference.org/satelliteevents.html
 
When Jul 2, 2014 - Jul 4, 2014
Where Bremen, Germany
Submission Deadline Feb 21, 2014
Notification Due Apr 1, 2014
Final Version Due May 1, 2014
Categories    robotics   artificial intelligence   sensor networks   multi-agent systems
 

Call For Papers

NEW === !! SUBMISSION OF FULL PAPERS STILL OPEN !! === NEW
Full papers can be still submitted without prior abstract submission until 21.2.2014. Acceptance decision will base on full paper submission.


Perceptive Robotics Embedded Symposium organized by
Prof. Dr. Frank Kirchner (DFKI RIC)
Prof. Dr. Michael Beetz (TZI, University of Bremen)
Dr. Stefan Bosse (WG Robotics, University of Bremen)

Topics of interest include, but are not limited to:

o Technologies for autonomous robots with advanced material- or structure-embedded perceptive capabilities like tactile sensing or sensorial structures.
o Development, application and electro-mechanical design of sensor networks in robot structures like tactile sensor arrays (artificial skin) embedded in robot hands/grippers.
o Robot control architectures, methods & algorithms using low-level perception provided by embedded sensor networks and linking perception with action.
o Adv. distributed/parallel data processing & communication in control architectures incorporating low-level perception, like data pre-processing on sensor node/micro-chip level.
o Biologically-inspired methods & architectures performing data pre-processing, reduction, filtering and computation of high-level information in large-scale sensor networks.
o Integrated data fusion of high-level perception like vision with raw low-level sensor data from embedded sensor networks using machine learning, neural networks, neuro-biological methods or evolutionary algorithms.
o Development of autonomous machines navigating independently without external reference.
o Machine/reinforcement learning methods for application in robot control enhancing autonomy.

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