works on
human-robot interaction 1policy clustering 1preference learning 1reinforcement learning 1reward modeling 1

From the 1 of 1 linked paper with an AI index.

collaborators

1 paper

cs.RO2026

Deployable Human Preference Alignment in Robotics: Learning Representative Rewards from Diverse Human Preferences

Taehyung Kim, Gwangmo Lee, Minjun Chang +2

The paper proposes Preference-based REward Clustering (PREC), a method that groups users with similar preferences and learns a compact set of reward models from binary feedback to…