Algorithmic Scenario Generation as Quality Diversity Optimization
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Abstract (arXiv)
The increasing complexity of robots and autonomous agents that interact with people highlights the critical need for approaches that systematically test them before deployment. This review paper presents a general framework for solving this problem, describes the insights that we have gained from working on each component of the framework, and shows how integrating these components leads to the discovery of a diverse range of realistic and challenging scenarios that reveal previously unknown failures in deployed robotic systems interacting with people.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 7 Sep 2024.
Builds on
21 of 28 references.
References · 21
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9 more
- 2023pyribs: A Bare-Bones Python Library for Quality Diversity Optimizationcited by 29
- 2022Covariance Matrix Adaptation MAP-Annealingcited by 24
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Citing papers per year: 2024: 1.
1 paper.