How do robots in a collective know what the group as a whole is doing? How can connected devices make sense of the world around them with so many interconnections? How can a robotic arm composed of many independent parts understand how its body is behaving as it reaches for an object? When intelligence is distributed across many parts, be they robots, devices, or objects, it can be tricky for the bigger picture to emerge. Yet answering these questions is key to making collective systems that are easy to design, monitor and control.
EMERGE will deliver a new philosophical, mathematical, and technological framework to demonstrate, both theoretically and experimentally, how a collaborative awareness – a representation of shared existence, environment and goals – can arise from the interactions of elemental artificial entities.
In this effort, we will rely only on unstructured conditions that the real world demands without leveraging a pre-existing shared language between them. Our goal is to surpass the limitations and barriers of the current state-of-the-art distributed systems to produce breakthroughs and open new markets in the next generation of robotic systems. The impact areas also include Internet-of-Things (IoT), smart cities, microservice-based information and communications technology (ICT) systems, and biomedical nanodevices.
The EMERGE consortium is composed of the University of Pisa (IT), Ludwig Maximilian University of Munich (DE), Delft University of Technology (NL), University of Bristol (UK), and Da Vinci Labs (FR). It has been awarded in the "Awareness inside" EIC Pathfinder Challenges 2021, a highly competitive grant in the Horizon Europe funding programme.
Funded by the European Union under Grant Agreement 101070918. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Innovation Council and SMEs Executive Agency (EISMEA). Neither the European Union nor the granting authority can be held responsible for them. In addition, UK partners will acknowledge being supported by UKRI grant number 10038942.