Robot Learning
Quality-Diversity Optimization
Quality-diversity optimization is a family of evolutionary methods that seeks a collection of solutions that are both high-performing and behaviorally diverse, rather than a single optimum. MAP-Elites, the best-known algorithm, maintains an archive of elite solutions across a grid of behavior descriptors. Cully et al.'s 2015 Nature paper used such archives to let a damaged hexapod rapidly find compensating gaits.
Why it matters for physical AI
Behavior repertoires give robots fallback strategies when hardware degrades or environments shift, and diverse solution archives seed adaptation far faster than reoptimizing from scratch.
Related terms
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