PREC: A Framework for Deployable Human Preference Alignment in Robotics

Jul 15, 2026

Researchers introduce PREC, a framework that clusters users by preference to learn representative reward models from sparse and noisy feedback. In simulated locomotion tasks, PREC groups users into preference-coherent clusters more accurately than baseline methods and improves social welfare metrics compared to both single shared-policy and per-user alignment approaches.

Why it matters: This work proposes a practical solution for aligning robot policies with diverse human preferences, addressing challenges of sparse and noisy feedback and reducing deployment validation burden.

Full story at: arXiv Robotics ↗