Autonomy will become central to future Cyber-Physical Systems, as we scale existing systems and connect multiple systems together to address challenges in domains such as health, transport, and energy. Modern artificial intelligence and machine learning technologies promise to improve every aspect of CPS such as maintenance, planning, optimization. CPSs can continually improve based on experience and adapt to changing circumstances. Machine learning is already being used for various aspects of CPS, from recognizing human activities to robotic control. Much of the prior work has focused on supervised learning, where labels for each input are provided by a human, and a model is trained that learns the patterns in the data. However, in many CPS scenarios, these labels are expensive to collect on a large scale (e.g., autonomous driving) or not known beforehand (e.g., power transmission levels). This workshop seeks contributions where the system can continually learn to improve based on experience and to adapt to changing circumstances, while being deployed in real physical and human environments and using methods such as model predictive control, reinforcement learning, and behavioral cloning. The CPS community is slowly adopting these methods with applications to autonomous driving, HVAC control, medical intervention, and urban planning. Autonomous CPSs bring in a variety of research challenges: specification of constraints such as traffic rules, safety, high-assurance, risk-sensitive behavior, explainability of the model, transfer from simulation of the physical world to the real-world, multi-agent coordination. While these topics are being studied in robotics conferences such as CoRL, they are only beginning to be explored in the much richer CPS domain and lack a current focused publication forum.

The Workshop on Autonomy in Cyber-Physical Systems seeks to bring together researchers to create solutions for the development of autonomous cyber-physical systems that can continually learn while being deployed in real physical and human environments. As CPS applications are typically safety-critical, we seek contributions that address safety and reliability concerns. As a goal of the workshop is to build a community of CPS researchers who are interested in frameworks, algorithms, tools, platforms, and testbeds for the development of autonomous cyber-physical systems, we seek contributions across disciplines - continual learning, reinforcement learning, control, human-machine interaction, safety, reliability, and verification. The workshop, in particular, encourages submissions that propose and explore new ideas as opposed to incremental research on established ideas.

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Topics of Interest:
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Continual learning in physical spaces
Reinforcement learning,
Imitation learning /behavioral cloning,
Model predictive control
Safe and efficient exploration
Transfer of models from simulation to real-world
Issues in partially autonomous CPS
Application spaces beyond robotics
Objective specification
Learning in the presence of constraints
Multi-agent and multi-objective systems
Interpretability/explainability of policy
Robustness to system changes
Verification of automation policies
Tools and platforms for autonomous learning

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Submission Instructions:
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The program committee will select the papers based on the novelty of ideas and technical depth. The papers will be selected for presentation as a mix of oral presentations and posters. Papers must be at most 6 pages, including figures, tables, and references and be typeset using the 2017 ACM Master article template.

Detailed typesetting instructions at https://docs.google.com/document/d/13_0jvJ3FVUSbT5RceXO1I09fZmowhDFULRfL8O9ND38

Papers may be submitted as a single PDF file at https://easychair.org/conferences/?conf=autocps2020.

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Contact:
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For more information see http://www.autocps.org or contact the organizers at chairs2020@autocps.org