AI RESEARCH
Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG
arXiv CS.AI
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ArXi:2603.09011v1 Announce Type: cross Robots that interact with humans must adapt to individual users' preferences to operate effectively in human-centered environments. An intuitive and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, e.g., trajectories, gestures, or voices. Existing techniques primarily focus on generating queries that optimize preference learning outcomes, such as sample efficiency or final preference estimation accuracy.