This approach improves learning in robots, indicating modular designs can enhance interactive skills and adaptability.
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.
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Amanda Prorok (2025) studied this question.
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