Article

One Policy, Many Styles: A Comparative Study of Preference Aggregation Locations in Steerable Humanoid Control

A study compared four preference aggregation methods—reward-level, advantage-level, loss-level, and PCGrad—across two humanoid robot control frameworks, finding that no aggregation level is optimal across all environments and metrics. The experiments used Preference Reward Responsiveness (PRR) combined with coverage and utility metrics; results show that early aggregation produces a wider range of behavioral responses but introduces more unintended objective interference, while later aggregation improves control precision at the cost of some responsiveness.

Part of the event: Comparison of Preference Aggregation Levels: No Single Approach Is Universally Optimal

This page provides a summary and source link only.