Impact Score 6.27
It is our pleasure to announce the Robotics and Autonomous Systems (RAS) special issue on Semantic Policy and Action Representations for Autonomous Robots (SPAR). 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. This special issue is a follow-up outcome of five IROS workshops held since 2015.
Special issue objectives
Autonomous robots are expected to perform a wide variety of everyday and specialised tasks in dynamic environments populated by humans and other artefacts. To perform human-centric collaborative tasks in such unstructured environments, robots will need to combine actions in intelligent ways to accomplish long-horizon, unseen tasks, while also communicating their intentions and capabilities to the humans with whom they share the environment. To enable this capability, robots must be endowed with knowledge of which actions they can perform, and an ability to reason about their consequences: both key elements of high-level cognitive behavior.
In contemporary robotics research, actions are interpreted in two main ways: first, as control policies responding to low-level sensor data; and second, as high-level symbolic actions. Action semantics can bridge these two levels, informing not just what to do but how to do it, and enabling effective human-robot collaboration in addition to autonomy. Recent advances in large-scale, general representation learning in computer cognitive vision, commonsense reasoning, and natural language processing indicates that the learned, general-purpose action semantics for robotics is on the immediate horizon. Deep semantic representation and reasoning mediated visual perception and action provides a tool for capturing the essence and function of actions, thereby helping robots learn and generalize across task and motion planning domains. High-level learned semantic action representations will yield robots with greater capability and autonomy in a wide range of naturalistic human environments.
This special issue aims to collect the most prominent research results in the important and growing area of action semantics. This includes the state-of-the-art in generic action representation and reasoning at the intersection of Vision, AI, and Robotics communities, particularly looking for a common ground to combine different approaches for generalizable autonomy. The special issue will particularly highlight new methods allowing robots to learn generalized semantic models for different domains, as well as scalability and adaptation of the learned models to new scenarios/domains.
Topics of interest
● Task and Motion Planning
● Explainable and Interpretable Robot Decision-Making methods
● Active and Context-based Vision
● Cognitive Vision and Perception - Semantic Representations
● Commonsense reasoning about space and motion (e.g., for policy learning)
● Task-oriented and Perception-informed Language Grounding
● Task and Environment Semantics
● Robot Learning from Demonstration and Exploration
RAS-SPAR Special issue URL
Complete details and future updates/announcements about the special issue are accessible through: https://sites.google.com/view/spar-2021/special-issue
■ Paper submissions open (through Elsevier system): Dec 1, 2021
■ Final paper submission deadline: Feb 15, 2022
Reviews of submitted papers will commence as the papers are submitted. Earlier submissions may expect an overall quick turn-around time. As a worst-case, we expect all accepted publications to be published in November 2022.
Karinne Ramirez-Amaro, Chalmers University of Technology, Sweden, https://www.chalmers.se/en/staff/Pages/karinne.aspx
Chris Paxton, Senior Robotics Research Scientist, NVIDIA, USA,
Jesse Thomason, University of Southern California / Visiting Academic, Amazon Alexa AI, USA.
Maru Cabrera, University of Washington, USA,
Mehul Bhatt, Örebro University / CoDesign Lab EU Sweden,
Contact: Please direct all inquiries pertaining to the special issue to [email protected]