Stephan Weiss, Alpen-Adria-Universität Klagenfurt ([email protected])
Paolo Robuffo Giordano, CNRS/IRISA ([email protected])
Valentin Peretroukhin, MIT ([email protected])
Jonathan Kelly, University of Toronto ([email protected])
Substantial advances have been made over the past two decades in the area of mobile robot autonomy, in part due to the development of sophisticated methods to integrate and evaluate data from multiple information sources. However, these gains come with the caveat that proper system initialization and calibration are essential. Starting with or quickly discovering the “right” initial conditions for estimation, planning, and control is a crucial but largely overlooked problem that has not yet been fully tackled by the community—instead it is often regarded as a post-hoc ‘engineering’ issue rather than a key safety concern. To date, researchers have primarily been concerned with long-term operation, often making the assumption that adaptation and learning may occur over a substantial time horizon. In fact, short-term competency and long-term adaptability are both critical for the widespread adoption of robotic systems, particularly in safety-critical domains. In a future where robots actively operate alongside people in human environments, machines will need to work correctly the first time, every time, anywhere, with minimal external (human) intervention.
This special issue aims to present state-of-the-art research related to power-on-and-go robots: robotic systems that are able to quickly deal with new situations and to adapt immediately to new environments, or to changes in their own operating parameters, with limited input data. The ability to operate correctly from the time the switch is flipped may mean the difference between successful task completion and catastrophic failure. Topics of interest include, but are not limited to:
• self-initialization, self-calibration, and self-healing systems,
• time-constrained reasoning and learning, rapid environment assessment,
• reliable and consistent state estimation from limited data,
• few-shot and single-shot online learning,
• integrity verification and assurance,
• formal and probabilistic methods with safety guarantees,
• cloud robotics solutions, and
• data-sharing in multi-robot systems.
The goal of the special issue is to approach the problem of power-on-and-go autonomy from a holistic perspective where theory and demonstrated experimental evaluation go hand in hand.
For more information, please contact [email protected]
Submission Deadline: November 15, 2020
First Reviews Completed: February 15, 2021.
Revised Submission Due: April 1, 2021.
Final Notification: May 15, 2021.
Submit manuscripts to: http://AURO.edmgr.com
Please choose “Special Issue on Power-On-and-Go” as the Article Type.
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
Papers must be prepared in accordance with the Journal guidelines: www.springer.com/10458
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