121 citations · 654 across the 70 of their papers we have counts for
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Soft Quality-Diversity Optimization
Saeed Hedayatian, Stefanos Nikolaidis
Quality-Diversity (QD) algorithms constitute a branch of optimization that is concerned with discovering a diverse and high-quality set of solutions to an optimization problem. Cur…
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
Benjamin Li, Shuyang Shi, Lucia Romero +7
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…
AutoQD: Automatic Discovery of Diverse Behaviors with Quality-Diversity Optimization
Saeed Hedayatian, Stefanos Nikolaidis
Quality-Diversity (QD) algorithms have shown remarkable success in discovering diverse, high-performing solutions, but rely heavily on hand-crafted behavioral descriptors that cons…
Multi-Objective Covariance Matrix Adaptation MAP-Annealing
Shihan Zhao, Stefanos Nikolaidis
Quality-Diversity (QD) optimization is an emerging field that focuses on finding a set of behaviorally diverse and high-quality solutions. While the quality is typically defined w.…
Integrating Field of View in Human-Aware Collaborative Planning
Ya-Chuan Hsu, Michael Defranco, Rutvik Patel +1
In human-robot collaboration (HRC), it is crucial for robot agents to consider humans' knowledge of their surroundings. In reality, humans possess a narrow field of view (FOV), lim…