Intention-Conditioned Flow Occupancy Models

Researchers introduce Intention-Conditioned Flow Occupancy Models (InFOM), a new reinforcement learning approach that uses flow matching to predict future states and incorporates user intention as a latent variable. The method demonstrates significant improvements with 1.8x median return improvement and 36% higher success rates across 40 benchmark tasks.