Periodic Reporting for period 2 - EMERGE (Emergent awareness from minimal collectives)
Periodo di rendicontazione: 2023-10-01 al 2025-03-31
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 behaves 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 easy to design, monitor and control.
The EMERGE project will deliver a new philosophical, mathematical, and technological framework to demonstrate, both theoretically and experimentally, how collaborative awareness – a representation of shared existence, environment and goals – can arise from the perceptions and interactions of individual agents, without leveraging a pre-existing common language between them. This collaborative awareness envisioned by EMERGE will transform robotic systems, as well as all kinds of applications which involve providing a service over a loosely coupled collective of entities, both physical or virtual, such as Internet-of-Things (IoT) devices, smart services, biomedical nanodevices, and many others.
The EMERGE project will deliver a new philosophical, mathematical, and technological framework to demonstrate, both theoretically and experimentally, how collaborative awareness – a representation of shared existence, environment and goals – can arise from the perceptions and interactions of individual agents, without leveraging a pre-existing common language between them. This collaborative awareness envisioned by EMERGE will transform robotic systems, as well as all kinds of applications which involve providing a service over a loosely coupled collective of entities, both physical or virtual, such as Internet-of-Things (IoT) devices, smart services, biomedical nanodevices, and many others.
Initially, EMERGE established "dimensional awareness" as a minimal operational capability for AI systems to adapt to and reflect dimensions like spatial proximity, decision confidence, or energy levels. This form of awareness, distinct from consciousness, is localized, scalable, and significantly enhances AI interactions and human-AI coordination. A preliminary engineering framework was developed to measure individual awareness dimensions in biological and artificial systems. Additionally, ethical analyses identified risks in human interactions with collectively aware AI, supported by empirical studies on human exploitation tendencies and responsibility attribution in AI systems. Computationally, EMERGE produced preliminary dynamical-system-inspired models crucial for adaptive processing in aware agents (the Archetypes Computing System) and described their collective behaviors.
Recently, EMERGE significantly advanced understanding and application of collaborative awareness in minimal artificial collectives. The refined multi-dimensional awareness framework now includes temporal, spatial, metacognitive, agentive, and self-awareness dimensions, enhancing collective adaptation and interaction. Ethical risks were further explored and documented through empirical experiments involving over 3,000 participants, leading to EU-aligned guidelines focusing on trust, transparency, and robustness. Development of the archetype computing vocabulary exceeded expectations, creating over 30 conceptual units and connectors, alongside novel adaptive mechanisms integrating lifelong learning and evolutionary strategies, validated through successful physical and computational demonstrations. The conceptual framework was also converted into engineering tools and methods for automatic control and monitoring of emergent awareness in robotic swarms, with practical demonstrations in manipulation tasks for cobots, logistics in robotic swarms, and soft robot dynamics characterization and control.
Recently, EMERGE significantly advanced understanding and application of collaborative awareness in minimal artificial collectives. The refined multi-dimensional awareness framework now includes temporal, spatial, metacognitive, agentive, and self-awareness dimensions, enhancing collective adaptation and interaction. Ethical risks were further explored and documented through empirical experiments involving over 3,000 participants, leading to EU-aligned guidelines focusing on trust, transparency, and robustness. Development of the archetype computing vocabulary exceeded expectations, creating over 30 conceptual units and connectors, alongside novel adaptive mechanisms integrating lifelong learning and evolutionary strategies, validated through successful physical and computational demonstrations. The conceptual framework was also converted into engineering tools and methods for automatic control and monitoring of emergent awareness in robotic swarms, with practical demonstrations in manipulation tasks for cobots, logistics in robotic swarms, and soft robot dynamics characterization and control.
Artificial intelligence nowadays enables the translation of isolated local awareness states from biological to artificial agents using information about the environment which can be collected from mechanical (contact, vibration, collision, etc), and electromagnetic (radio, infrared, visible light, etc) stimuli. However, the cooperation among those units is often dependent on some kind of central processing unit which collects that information and establishes a centralised awareness which distributes commands to each unit. Although useful, this process allows for very specific and previously programmed situations to be navigated and problems to be solved. For artificial agents to be able to act in the unstructured conditions that the real-world demands, a new concept of collaborative awareness is needed. EMERGE’s goal is to establish, analyse, implement and test a new artificial intelligence framework that allows this collaborative awareness to emerge from the interplay of multiple individual units of local awareness. This collaborative awareness becomes an emergent process supporting complex, distributed, and loosely coupled systems capable of high degrees of collaboration, self-regulation, and interoperability without predefined protocols.
In terms of innovation impact, EMERGE aims to surpass limitations and barriers of the current state-of-the-art multi-agent collaborative systems, with potential to produce breakthroughs and open new markets in the next generation of robotic systems. For that, the project focuses on three use cases. The first use case is modular soft robots – self‐assembling, repairing or replicating robots made from soft materials which offer high freedom of movement, even in confined spaces, and better manipulation of delicate objects. In these robots, the body formed by a physically distributed collective needs to self-organise to account for the dynamic addition of components. The second use-case are robotic swarms – groups with a large number of robots whose behaviour arises from the interactions between themselves and with their environment. This is an example of a large-scale minimal collective where agents need coordination to achieve a collaborative goal. Finally, the third use case are collaborative robots, or cobots – robots interacting in direct contact with, or in close proximity to, humans. These represent a closer-to-market use case where interoperability is currently a significant barrier. While robotics provides the perfect testing ground for this new framework, EMERGE also envisions impact in areas such as Internet-of-Things (IoT), smart cities and transportation, microservice-based information and communications technology (ICT) systems, and biomedical nanodevices, among others.
In terms of innovation impact, EMERGE aims to surpass limitations and barriers of the current state-of-the-art multi-agent collaborative systems, with potential to produce breakthroughs and open new markets in the next generation of robotic systems. For that, the project focuses on three use cases. The first use case is modular soft robots – self‐assembling, repairing or replicating robots made from soft materials which offer high freedom of movement, even in confined spaces, and better manipulation of delicate objects. In these robots, the body formed by a physically distributed collective needs to self-organise to account for the dynamic addition of components. The second use-case are robotic swarms – groups with a large number of robots whose behaviour arises from the interactions between themselves and with their environment. This is an example of a large-scale minimal collective where agents need coordination to achieve a collaborative goal. Finally, the third use case are collaborative robots, or cobots – robots interacting in direct contact with, or in close proximity to, humans. These represent a closer-to-market use case where interoperability is currently a significant barrier. While robotics provides the perfect testing ground for this new framework, EMERGE also envisions impact in areas such as Internet-of-Things (IoT), smart cities and transportation, microservice-based information and communications technology (ICT) systems, and biomedical nanodevices, among others.