[robotics-worldwide] [journals] CfP: Special Issue on "Semantic Policy and Action Representations for Autonomous Robots (SPAR)"

Eren Erdal Aksoy aksoyeren at gmail.com
Mon Apr 16 06:45:03 PDT 2018

 [journals] RAS special issue: Semantic Policy and Action Representations
for Autonomous Robots (SPAR) -- *Deadline April 20, 2018*

**** Robotics and Autonomous Systems
             Special Issue on
Semantic Policy and Action Representations for Autonomous Robots***
----Call for Papers---

It is our pleasure to announce the Robotics and Autonomous Systems (RAS)
special issue on Semantic Policy and Action Representations for Autonomous
Robots (SPAR). This special issue is a follow-up outcome of two successful
IROS workshops held in 2015 and 2017. We would like to invite all
interested researchers to submit their papers in the areas of reasoning,
perception, control, planning, and learning applied to robotic systems.

***RAS-SPAR Special issue URL***
Contact email:  spar.workshop at gmail.com

*** Important Dates ***:
Paper submission deadline (EXTENDED): 20th April 2018
Notification of acceptance: 15th June 2018
Final Submission:  3rd August 2018
Publication date: September 2018

*** Special issue objectives ***

Service and industrial robots are expected to be more autonomous and work
effectively around/ alongside humans. This implies that robots should have
special capabilities, such as interpreting and understanding human
intentions in different domains. The major challenge is to find appropriate
mechanisms to explain the observed raw sensor signals such as poses,
velocities, distances, forces, etc., in a way that robots are able to make
informative and high-level descriptive models out of that. These models
will, for instance, permit the understanding of, what is the meaning of the
observations/demonstrations, infer how they could generate/produce a
similar behavior in other conditions/domains?, and more importantly, allow
robots to communicate with the user/operator about why they infer that
behavior. One promising way to achieve that is using high-level semantic
representations. Several methods have been proposed, for example,
linguistic approaches, syntactic approaches, graphical models, etc.

This special issue is focused on highlighting the recent developments in
semantic reasoning representations and semantic policy generation from low
level (sensory signal) to high level (planning and execution). More
importantly, this special issue will gather information about various
bottom-up and top-down approaches for semantic action perception and
executions in different domains. Furthermore, we are aiming to compare
various state-of-the-art approaches for generic action and reasoning
representations in both computer vision and robotic communities, looking
for a common ground to combine assumable different approaches for
autonomous capability and reliability. Overall, this special issue aims to
present the main benefits of this new emerging type of methods such as
allowing robots to learn generalized semantic models for different domains
as well as the next breakthrough topics in this area, e.g. the scalability
of the learned models that can adapt to new scenarios/domains in a way that
the robot can transfer all the acquired knowledge and experience from
existing data to new domains with very little human intervention.

Topics of interest include, but are not limited to:
 *AI-Based Methods
    --Learning and adaptive systems & Probability and statistical methods
    --Action grammars/libraries  & Spatiotemporal event encoding
    --Machine learning techniques for semantic representations
 *Reasoning Methods in Robotics and Automation
   --Signal to symbol transition (Symbol grounding) & Different levels of
   --Semantics of manipulation actions & Semantic policy representation
   --Context modeling methods
 *Human Behavior Recognition
   --Learning from demonstration & Object-action relations
   --Bottom-up and top-down perception
 *Task, Geometric, and Dynamic Level Plans and Policies
   --PDDL high-level planning & Task and motion planning methods
 *Human-Robot interaction
   --Prediction of human intentions & Linking linguistic and visual data

*** Guest editors ***

Karinne Ramirez-Amaro, Technical University of Munich,
Yezhou Yang, Arizona State University, USA, https://urldefense.proofpoint.com/v2/url?u=https-3A__yezhouyang&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=31_-273IToZAQkrZD3nK7bogrdUBYrfBu5INQe0x04w&s=DCGUuvAvL1UGFIYXh4j21kNvjk09r2SLerZ9nOVIlmY&e=.
Neil T. Dantam, Colorado School of Mines, USA,  https://urldefense.proofpoint.com/v2/url?u=http-3A__www.neil.dantam.name_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=31_-273IToZAQkrZD3nK7bogrdUBYrfBu5INQe0x04w&s=y6DryEOw4s7UbG_naD8_NeLGpa9tlsySWqwu5PZpJh8&e=

Eren Erdal Aksoy, Halmstad University, Sweden, https://urldefense.proofpoint.com/v2/url?u=http-3A__islab.hh.se_mediawiki_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=31_-273IToZAQkrZD3nK7bogrdUBYrfBu5INQe0x04w&s=L4KLUvrqqrqbBiNKghHwlfxE3nlra9PtStWyaXjenWA&e=
Gordon Cheng, Technical University of Munich, https://urldefense.proofpoint.com/v2/url?u=https-3A__www.ics.ei.tum.de_en_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=31_-273IToZAQkrZD3nK7bogrdUBYrfBu5INQe0x04w&s=Gzqplk4Cv1Vwtbk6eg4ugukUjOkJArLkY5vlna9HVy4&e=

All the best
Eren Aksoy


Eren Erdal Aksoy
Assistant Professor
Halmstad University (HH)
School of Information Technology (ITE)
Center for Applied Intelligent Systems Research (CAISR)

P O Box 823, Room E526
SE-301 18 Halmstad, Sweden

Office: +46 35-1671 21
Mobile: +46 729 77 36 51
E-Mail: eren.aksoy at hh.se
Web: https://urldefense.proofpoint.com/v2/url?u=http-3A__islab.hh.se_mediawiki_Eren-5FErdal-5FAksoy&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=31_-273IToZAQkrZD3nK7bogrdUBYrfBu5INQe0x04w&s=tfGDV21Kq1fsOTmD2-12XZyVLiEt7z26Jj7PWd3qSDw&e=

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