Special Issue "Semantic Policy and Action Representations for Autonomous Robots"

 

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.

Important Dates

Paper submission deadline: 25th March 2018

Notification of acceptance: 15th June 2018

Final Submission: 3rd August 2018

Publication date: September 2018

 

 

Contact email: spar.workshop@gmail.com  

Guest editors

Karinne Karinne Ramirez-Amaro, Technische Universität München, Germany
Yezhou Yezhou Yang , Arizona State University, USA
Erdal Erdal Aksoy , Lund University, Sweden
Neil Neil T. Dantam , Colorado School of Mines, USA
Gordon Gordon Cheng, Technische Universität München, Germany

Objectives

This special issue will present the main benefits of this new emerging type of methods, such as allowing robots to learn generalized semantic models for different domains. We will also like to discuss the next break-through 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.

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.

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Topics of Interest

The topics that are indicative but by no means exhaustive are as follows:

  • AI-Based Methods
    • Learning and Adaptive System
    • Probability and Statistical Methods
    • Action grammars/libraries
    • Machine learning techniques for semantic representations
    • Spatiotemporal event encoding
  • 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 method
  • 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

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