********************************************************************************************** **** 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*** http://www.ics.ei.tum.de/en/www.ics.ei.tum.de/workshopiros/ras-special-issue...

Contact email:  s***********p@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