[robotics-worldwide] [meetings] ICNSC 2017 Special Session on Advanced Learning Techniques for Autonomous Robots in the Internet of Things - Call For Papers

Massimiliano De Benedetti m.debenedetti at dmi.unict.it
Fri Nov 11 01:21:21 PST 2016

*"Advanced Learning Techniques for Autonomous Robots in the Internet of
Things (ALTARIoT)"*
<https://urldefense.proofpoint.com/v2/url?u=http-3A__icnsc2017.dimes.unical.it_ALTARIoT.html&d=DgIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=DsysMzm_eXiuXDy-oxEc6qLcSa4Ob3nodJzFX4Zrhuo&s=7IpN8csQlQcg3T1bB-Nvd5rhG-zND7eM7MDjsQ1ydhA&e= >*

In the past twenty years, Machine Learning (ML) has enabled a number of key
technologies that have revolutionized many aspects of our daily lives.
Notable examples include spam filtering, automated fraud detection, face
recognition, predictive medicine.

In addition, due to the exponential growing of interests and applications
for IoT, ML-based systems have evolved to the point of being able to make
sense of complex and huge sets of data collected by IoT devices, and derive
meaningful decisions: large-scale recommender systems provide buying
suggestions to online shoppers, and self-driving vehicles can
algorithmically predict whether a pedestrian will cross and stop if

Furthermore, because of their inherent ability to deal with complex
information, ML techniques find a natural application in the creation of
autonomous robotic/multi-agent systems. The overarching hypothesis is that
ML will facilitate the creation of robotics systems that can autonomously
operate in complex environments by exploiting data collected by IoT
devices, adapt to changing circumstances and predict/avoid dangerous

This special session will solicit contributions that identify the
challenges related to the application of ML (particularly Deep learning)
techniques to robotic/multi-agent systems fully connected as IoT devices
and propose novel methods to enhance the autonomous capabilities of robots
and agents.

The topics of interest for contributed papers comprise, but are not limited

   - Deep learning and Machine learning for perception, action, and control
   in robotics/multi-agents contexts
   - Deep learning and machine learning for embedded systems or platforms
   with limited computational power
   - Deep learning and Machine learning for Internet of Robotics Things and
   and multi-agent systems
   - Learning techniques for sensor data fusion in IoT
   - Reinforcement Learning and Adaptive Control for Internet of Robotics
   - Software architectures to support learning techniques in robotics and
   - Programming languages for learning techniques in robotics and IoT
   - Cloud computing to support learning in IoT and robotics
   - Fog Computing software architectures for IoT
   - Imitation Learning
   - Multi-agent Learning
   - Using robotic technology and multi-agent systems to create novel
   datasets comprising interaction, vision, navigation data, sensors data etc.
   - Simulations and related tools for IoT connected autonomous robots

All accepted papers, that will be part of the conference proceedings, are
expected to be included in IEEE Xplore and will be indexed by EI.

SubmissionSubmitted manuscripts should be within six (6) pages in IEEE
two-column format, including figures, tables, and references. Please use
the templates at Manuscript Templates for IEEE Conference Proceedings
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.ieee.org_conferences-5Fevents_conferences_publishing_templates.html&d=DgIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=DsysMzm_eXiuXDy-oxEc6qLcSa4Ob3nodJzFX4Zrhuo&s=2kaoGn2CwhDQM8SijuhJ8Zemh1PT_abks5BXwmm_8wk&e= >
the conference website to prepare your paper. All submissions MUST be in
PDF format.
Complete manuscripts must be electronically submitted through easychair at:
Please, during the submission process specify this Special Session as topic SS1
- ALTARIoT in easychair.

Important Dates
January 15, 2017 Paper submission due
February 28, 2017 Notification of acceptance
March 15, 2017

Massimiliano De Benedetti Camera-ready copy due

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