Extending robot minds through collective learning.
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- Record sourced from PubMed, PMID 40991715.
- Also identified by DOI 10.1126/scirobotics.adv4049.
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Abstract
The current trend toward generalist robot behaviors with monolithic artificial intelligence (AI) models is unsustainable. I advocate for a paradigm shift that embraces distributed architectures for collective robotic intelligence. A modular "mixture-of-robots" approach with specialized interdependent components can achieve superlinear gains, offering benefits in scalability, adaptability, and learning complex interactive skills.