[robocup-athome] [journals] RAS special issue: Semantic Policy and Action Representations for Autonomous Robots (SPAR)

Karinne Ramirez Amaro karinne.ramirez at tum.de
Wed Nov 15 09:32:34 UTC 2017


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**** Robotics and Autonomous Systems
              Special Issue on
Semantic Policy and Action Representations for Autonomous Robots***
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----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***
http://www.ics.ei.tum.de/en/www.ics.ei.tum.de/workshopiros/ras-special-issue/

Contact email:  spar.workshop at gmail.com

*** Important Dates ***:
Paper submission deadline: 25th March 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 abstraction
    --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, 
https://www.ics.ei.tum.de/people/ramirez
Yezhou Yang, Arizona State University, USA, 
https://yezhouyang.engineering.asu.edu
Neil T. Dantam, Colorado School of Mines, USA, http://www.neil.dantam.name/
Eren Erdal Aksoy, Lund University, Sweden, 
http://www.lunduniversity.lu.se/lucat/user/er0201ak
Gordon Cheng, Technical University of Munich, 
https://www.ics.ei.tum.de/en/people/cheng

-- 
Dr.-Ing. Karinne Ramirez Amaro		
Institute for Cognitive Systems	
Technische Universitaet Muenchen	

Address: Karlstr. 45, 2.OG. 80333 Munich, Germany
Room: 2008
Web page: http://web.ics.ei.tum.de/~karinne/Ramirez/index.html
email: karinne.ramirez at tum.de
Telephone: +49-89-289-26791



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