MMLDT Conference – Autonomy and Me: Working Together for a Better Future

Congratulations to Mr. Daniel Mitchell, Mr. Sam Harper and Prof. David Flynn in their recent presentation at the Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT) Conference. This conference was held over September 26-29, 2021 and virtually in Hyatt Regency Mission Bay, San Diego, CA.

Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations.

Watch their presentation below (DOI: https://doi.org/10.26226/morressier.612f6737bc981037241008a1).

Abstract: The transition of disruptive technologies into society and industry has always required trust and acceptance from end users. Unlike prior technological revolutions, autonomous systems are associated with the displacement of people, creating unease in how to prepare individuals, communities, industry and government. We present how autonomous systems are enablers to unlocking human potential, supporting the creation of a sustainable and prosperous future. Using the context of the Offshore Renewable Energy (ORE) sector we demonstrate how our Symbiotic System Of Systems Approach (SSOSA) can create autonomous robotic assistants that protect and collaborate with people, creating high value jobs within a symbiotic partnership that delivers mutually optimized benefits, with an emphasis on transferability across people and place. Within multiple high value industries and future societal services, digitalization, artificial intelligence and autonomous systems are omni-present in advancing current capabilities. In the context of our research, we focus on the ORE market, which represents an early adopter for the aforementioned technologies. Trustworthy autonomy is an elemental area of research, though lacking sufficient advancement to be applied with high fidelity, supports how people and society may be empowered by advances in robotic automation. The complex interaction dynamics of human-machine systems today also mean digitalization is inevitable if we are to connect business, people, place, and technology. As levels of automation increase, so do legal issues with accountability and ethics. How then can we develop autonomous systems that gain trust from users? And how can such systems adapt and provide insights to better develop symbiotic autonomy that can be entrusted with decision making? How can we bridge institutional boundaries where trust in robotics and autonomous systems is better understood by both researchers and members of the public? Our Symbiotic Digital Architecture (SDA) integrates current state-of-the-art technologies to address operational and resilience requirements enhancing trusted autonomy. Our SSOSA reflects the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. The resultant Human-Society-Machine-Infrastructure Interface provides a holistic overview, focusing on the functional, operational, safety and planning requirements within an interactive and bidirectional digital twin. This ensures the certification of infrastructure and machine via hyper enabled human interaction capabilities under a single symbiotic framework. Supported decision making for the remedial maintenance and remaining useful life of assets is represented in our applied SSOSA as an improvement for the human-in-the-loop understanding state of health data, including infrastructure, environment and robotic platform.

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