Swarms of Simple Machines: How Europe’s EMERGE Project Built Awareness Without a Brain

When artificial intelligence leaves the laboratory and steps into the messy, unpredictable world of human environments, the questions it raises stop being purely technical. How does a machine understand its surroundings? How does it act on its own, and how does it behave around people? A four-year European research consortium called EMERGE, funded under the European Innovation Council’s Pathfinder programme, has spent the past several years answering those questions in an unconventional way: by refusing to build a bigger brain. Instead of concentrating intelligence in one complex central system, the project bet on the simplest artificial agents imaginable—small robots in a swarm, individual components of a robotic body, connected devices scattered across an environment—and asked what could emerge when they began to talk to each other.

The bet paid off. Individually, each of these units possesses limited intelligence and only fragmentary information about the world. Together, however, by exchanging local signals and coordinating their behaviour, they can build a shared representation of their existence, their environment and their goals. The researchers call this collaborative awareness, and it became the conceptual backbone of the entire project. The consortium brought together an unusually broad set of disciplines: artificial intelligence and robotics researchers from the University of Pisa, philosophers and cognitive scientists from Ludwig Maximilian University of Munich, swarm robotics specialists at the University of Bristol, soft robotics experts at Delft University of Technology, and the French research hub Da Vinci Labs. Over four years, this group produced not just papers, but a philosophical, mathematical and technological framework for awareness in artificial systems—along with robotic prototypes, more sustainable AI architectures, startup ventures and follow-up European projects.

One of the project’s most important contributions is a careful conceptual distinction that separates awareness from consciousness. Consciousness, in the philosophical sense, is generally associated with subjective or phenomenal experience: having feelings, or experiencing the world from a particular first-person perspective. Awareness, as the EMERGE team defines it, is something far more tractable. “In EMERGE, we understand awareness more operationally, as the capacity of an agent to process and integrate information in a way that is relevant to its actions,” explains Ophelia Deroy, philosopher and cognitive scientist at LMU Munich. Crucially, the project’s experiments showed that people can understand an artificial system as aware without ever assuming that it has subjective experience. That separation matters enormously for public perception and for the ethics of human–machine interaction, because it allows engineers to build and describe aware machines without drifting into claims about machine sentience.

To make the concept scientifically useful rather than merely evocative, the researchers decomposed awareness into distinct dimensions that can be applied across individuals and collective systems: spatial awareness, temporal awareness, self-awareness, agentive awareness and metacognitive awareness. Rather than treating awareness as an all-or-nothing property—a binary switch that a system either possesses or lacks—the framework ties each dimension to specific capabilities and concrete tasks. This move transforms a philosophical puzzle into an experimental programme. It becomes possible, for the first time, to test empirically whether increasing a system’s awareness along a particular dimension actually improves its performance on a defined task, and to measure by how much. The result is a research methodology in which awareness is not asserted but demonstrated, dimension by dimension, capability by capability.

The mathematical and computational core of the project addresses a deceptively simple question: how can awareness emerge across a physically distributed system in which no single agent sees the whole picture? The EMERGE framework describes how simple local information, exchanged with neighbouring agents, can be integrated so that the collective coordinates its actions toward solving a task. “The beauty of the approach is that the system operates in a distributed way,” says Sabine Hauert, professor at the University of Bristol. “This way systems that can scale to large numbers of agents and remain operational when individual robots fail or conditions change. It also allows humans to interact with the systems as a coordinated collective rather than controlling each robot separately.” In practical terms, that means a swarm does not collapse when one of its members breaks down, and a human supervisor can issue intentions to the group as a whole instead of micromanaging every unit.

At Delft University of Technology, the framework was pushed toward physical robotic bodies. “We want robots that can understand how their actions affect the world, anticipate what people and other robots are doing, and adapt accordingly,” explains Cosimo Della Santina, associate professor at TU Delft. “That is a key step toward robotic systems that can operate more autonomously and work naturally alongside humans.” This anticipation of consequences—understanding how one’s own movements reshape the environment, and predicting the trajectories of both human partners and fellow machines—is precisely the kind of integrated, action-relevant information processing that the project’s operational definition of awareness was built to capture. It is also what separates a genuinely collaborative robot from a pre-programmed arm repeating fixed motions.

The consortium’s mathematical framework also delivered an unexpected dividend for machine learning itself: parsimony. Systems built on the EMERGE principles proved capable of learning from substantially smaller quantities of data than conventional architectures require. “Many AI systems today depend on large, centralized computing infrastructures,” says Claudio Gallicchio, associate professor at the University of Pisa. “Our results open the way to a more decentralized and environmentally sustainable models, in which data can be processed locally using small and energy-efficient devices, such as neuromorphic hardware, reducing energy consumption, network traffic and latency while improving privacy.” In an era when training frontier models consumes enormous energy budgets and concentrates computational power in a handful of data centres, the prospect of aware, adaptive systems running on small, low-power edge hardware carries implications far beyond robotics—touching sustainability, data sovereignty and the geopolitics of computing.

The project also confronted the human side of the equation, and the findings are sobering. Bahador Bahrami, director of the Crowd Cognition group at LMU Munich, notes that “our work also examined the ethical side of human interaction with collaboratively aware systems, with important implications for future environments in which groups of humans and artificial agents will have to interact, negotiate and cooperate.” Among the studies was research into how people behave toward automated systems such as self-driving cars. The result: users may be more willing to take advantage of artificial agents than of human counterparts—a phenomenon the researchers describe as algorithmic exploitation. People who would never cut in front of another driver may happily exploit a machine, precisely because it cannot be offended, retaliate, or hold them socially accountable. Understanding this asymmetry, the team argues, is essential for designing and governing artificial systems that interact directly with people.

EMERGE was deliberately structured as a pathway from foundational research to technological innovation, and its results are already spinning out into ventures. Embodied AI develops efficient AI models for future generations of humanoid robots. ContinualIST builds AI models efficient enough to run on smaller, lower-power hardware. Collective Robotics focuses on swarms of robots that coordinate and operate collectively, and Phoebe is dedicated to systems in which humans and artificial agents solve problems together. The concepts will also anchor new research projects: EIC Pathfinder SWIFT-BUILD will apply collaboration among multiple robots and humans to automated construction, while HE MINGLE and ADA-COLLAB investigate how general-purpose, agentic AI systems can form societies, collaborate with one another, and interact with humans. Even public engagement got its own artefact: nOdes, an artistic installation in which an interactive swarm exchanges information and changes its behaviour in response to both its agents and its human visitors, letting anyone experience collaborative awareness firsthand.

“EMERGE started from a fundamental question about awareness, but it has ended with very concrete results: new robotic systems, more sustainable AI architectures, new startup initiatives and follow-up projects that are already carrying forward the idea of building artificial systems that are more efficient, sustainable, collaborative, and imbued with human values,” concludes Davide Bacciu, professor at the University of Pisa and coordinator of the project. The project was funded by the European Union under Grant Agreement 101070918, with UK participants supported by UKRI grant number 10038942. Its legacy is a reframing of one of technology’s oldest ambitions: intelligence may not need to be concentrated, enormous and opaque. It can be distributed, modest, energy-lean and—perhaps most importantly—legible to the humans who live alongside it. The swarms are small, but the idea they carry is anything but.

Subject of Research: Collaborative awareness in distributed artificial agents and swarm robotics

Article Title: European project EMERGE concludes advancing collective awareness in artificial systems

Article References: European project EMERGE concludes advancing collective awareness in artificial systems. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: EMERGE project, collaborative awareness, swarm robotics, distributed AI, EIC Pathfinder, machine learning, neuromorphic hardware, human-robot interaction, algorithmic exploitation, sustainable AI, philosophy of mind, cognitive science