The study, published in Nature Communications, reveals that swarms of robots can self-organize and adapt using the same local interactions that drive animal group behavior.
Nature-Inspired Intelligence in Motion
For millennia, evolution has shaped collective behaviors in nature, from bird flocks to insect swarms, that solve complex problems through decentralized coordination. These animal groups operate as if they were “living materials,” capable of reorganizing, sensing, and responding to their environment in real time.
Inspired by these systems, scientists are using robotics to test and apply the same principles. Swarm robotics borrows from nature’s design, creating machines that can collectively sense, decide, and adapt. In turn, these robotic systems allow researchers to validate biological theories in controlled conditions, forming a powerful feedback loop between biology and engineering.
How Collective Decisions Emerge
The research highlights how both animal groups and robot swarms make decisions using feedback loops rather than commands from a leader. Each individual processes local information and influences its neighbors, allowing the group to reach consensus efficiently.
In honeybee colonies, for instance, scouts perform waggle dances to recruit others to a preferred site while issuing stop signals to discourage competing choices. Similar dynamics appear in fish schools and bird flocks, where alignment and motion patterns guide collective direction without explicit signaling.
Robot experiments show the same rules at work: by combining active inhibition with the recruitment of uninformed agents, swarms reach reliable, fast consensus. This non-linear feedback improves decision speed and group coordination, even if it sacrifices a small degree of accuracy, just as in nature.
From Living Collectives to “Intelligent Matter”
The team’s findings point toward a new class of swarm systems known as intelligent matter: large groups of robots that behave like programmable materials. These systems can switch between fluid-like and solid-like states, morph their shape, and coordinate movement through local interactions.
Recent advances include modular robots that can self-replicate and assemble into larger formations, as well as “Robo-matter,” a swarm of thousands capable of flowing, repairing, and transporting objects collectively. At the microscopic scale, engineers have built 8-micron ant-inspired microrobots guided by magnetic and optical fields, which can self-assemble into microstructures for potential use in drug delivery and micro-manipulation.
The study also underscores how robotics is helping biologists probe the mechanics of animal behavior. For example, experiments with cyborg cockroaches - whose natural movement is guided electronically - show how engineered systems can interact directly with living organisms.
This collaboration is changing how scientists model behavior. Earlier theories treated animals as particles obeying fixed rules, but newer frameworks incorporate sensing and cognition, recognizing that animals constantly interpret and respond to their surroundings. Robotics, with its precision and controllability, provides a new way to test these evolving theories.
Toward Systems That Learn and Adapt Together
Both biological groups and robotic swarms rely on the same core idea that local interactions drive global behavior. There’s no central controller. Instead, each individual responds to its surroundings and neighbors, and from those simple rules, coordinated group behavior emerges.
By studying how animals do this, researchers are designing robots that can work together in similar ways. These systems can respond to changes, learn from their environment, and adjust as needed.
This approach could be useful in real-world settings where conditions are unpredictable or spread out, like monitoring environmental changes, exploring hard-to-reach places, or managing distributed systems such as agriculture or supply chains. Just as importantly, the work helps biologists test ideas about how collective behavior works in nature.
Learning from Life to Build Smarter Systems
By learning from life, researchers are developing robotic systems that can adapt, coordinate, and respond to change without central oversight. These swarm-based approaches could prove invaluable in environmental monitoring, exploration, and resource management settings where flexibility and resilience are key.
Beyond applications, the work reveals something deeper in the fact that intelligence itself can emerge from the bottom up. Whether in a school of fish or a swarm of robots, complex problem-solving begins with simple interactions, and nature may already hold the instruction manual.
Journal Reference
Couzin, I. D. (2025). Collective intelligence in animals and robots. Nature Communications, 16(1), 9574. DOI:10.1038/s41467-025-65814-9. https://www.nature.com/articles/s41467-025-65814-9
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