[Event at CIG] [CFP][meetings] FINAL CALL: Submit to Autonomous Robots & Multirobot Systems (ARMS) Workshop at AAMAS 2024 by Feb 20

[Nicola Basilico]

We invite you to submit your work to a full-day workshop on Autonomous Robots and Multirobot Systems (ARMS) to be held as part of AAMAS 2024, and to accompany the Robotics Area of Interest of the main conference.

Workshop Website: https://arms2024.di.unimi.it/

AAMAS 2024 Website: https://www.aamas2024-conference.auckland.ac.nz/

** Important Dates **

Paper submission deadline: February 20, 2024 (final deadline)

Notification of acceptance: March 4, 2024 (temptative)

** Overview **

Robots are agents, too. Indeed, agents researchers often use robotics problems as motivating examples. Both practical and analytical techniques developed by agents researchers influence, and are influenced by, research in autonomous robots and multi-robot systems. Despite the overlap between the agents and robotics research areas, researchers from these communities only have a few opportunities to meet and interact. The Robotics Area of Interest in the main AAMAS conference (formerly, the “Robotics Track”) is one such opportunity. The goal of this workshop is to build on this opportunity, offering an informal and dedicated forum where agents and robotics researchers can interact, discuss promising research directions and open problems, and foster further collaborations.

Contributions are sought in all areas of robotics, in particular those that are related to autonomous agents research. Theoretical papers are welcome, as long as they clearly specify the connection to challenges in robotics. Empirical studies should ideally present experiments with real robots, although physical simulation studies are also acceptable. Papers that focus on mechanical aspects and low-level control should make an effort to relate this work to the agents community. Areas of particular interest include (but are not limited to):

- Knowledge representation and reasoning in single- and multi-robot (agent) settings
- machine learning in single- and multi-robot (agent) settings
- single- and multi-robot (agent) planning
- human-robot (agent) interaction and teamwork
- multi-robot teamwork
- game-theoretic coordination
- market-based methods for coalition formation and task allocation
- large-scale multi-robot systems and swarms
- imitation and learning by demonstration/example

We particularly welcome papers with accompanying demos of real robots, which can come as recorded videos (please use YouTube or general-purpose repositories to share the video if it is ready at the submission time) or as live demos during the workshop.

** Submissions details**
https://easychair.org/conferences/?conf=arms2024

Submission should follow AAMAS 2024 format (see https://www.aamas2024-conference.auckland.ac.nz/calls/submission-instruction/ for instructions or directly download the LaTeX style file: https://www.aamas2024-conference.auckland.ac.nz/files/2023/09/AAMAS-2024-Formatting-Instructions-CCBY.zip)
The length of each paper including figures may not exceed 8 pages (plus any number of pages for references)

Papers do not have to be anonymized; we will follow a single-blind review process. Also, there will be no archival proceedings; papers will be made available to the workshop participants.

** Fast-Track Review Process **
As part of our coordination with the main AAMAS conference, we offer a fast-track review process to papers that have been accepted as extended abstracts ("short papers”) to the Robotics Area of Interest of the main AAMAS conference. For such papers, please include the original AAMAS paper number with the submission.

** Organizing Committee **
Nicola Basilico, University of Milan, Italy
Mohan Sridharan, University of Edinburgh, UK
Noa Agmon, Bar Ilan University, Israel
Francesco Amigoni, Politecnico di Milano, Italy
Joydeep Biswas, University of Texas at Austin, USA
Alessandro Farinelli University of Verona, Italy
Maria Gini University of Minneapolis, USA

The contact organizers are Nicola Basilico (nicola.basilico at unimi.it) and Mohan Sridharan (m.sridharan at bham.ac.uk).