We are pleased to share our latest research, now published in Nature Communications: “Smart Cellular Bricks: Physical Modules That Recognize Their Own Shape and Repair Themselves.”
Blog: https://t.co/FMkbCv8mpP
Paper: https://t.co/rxzbpU3zTu
A long-running theme in our work is collective intelligence: the idea that sophisticated, robust behavior can emerge from many simple parts following local rules, with no central controller, as it does in a colony, a tissue, or a brain. We had mostly studied this in software and simulation. So this time we asked a simple question. Do the same decentralized principles hold up in the physical world, where communication is noisy and modules fail?
To find out, we built a collection of simple cubic bricks. Each brick runs the same small neural network and talks only to the bricks it is physically connected to. No brick is told its position, or which shape it is part of.
Yet from these purely local exchanges, the collective converges on the correct global shape, locates where modules are missing or damaged, and can even guide its own repair, inspired by how living tissue self-organizes and regenerates after injury.
For us, this is a first step in a broader direction: taking the principles of collective intelligence we have studied in software and letting them emerge, decentralized and robust, in the physical world. In the future, we imagine smart materials that let structures sense and report damage on their own, and LEGO-like systems that recognize their own configuration and adapt in real time, pointing toward environments that are more robust, adaptive, and regenerative.
This work is a collaboration between Sakana AI, IT University of Copenhagen and Autodesk.
An interesting part of this work is autonomous damage recovery.
We trained the cells to not only recognize their shape, but also detect missing neighbors across six spatial directions with 95% accuracy.
As shown in the animation, starting from just a small seed cluster, the blocks can repeatedly add new modules in the indicated directions to fully regrow a chair, table, and plane.
In simulation, we found this approach scales well to complex, hollow geometries with over 18,000 cubes. It is a practical step toward physical systems that can regenerate themselves after injury.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
Agents with identical weights and neighbor-only messaging solve a global inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → task with no central controller, and the damage-detection result shows the scheme holds up when modules go missing.
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