ACID: Action Consistency via Inverse Dynamics for Planning with World Models
ACID is a decision-time planning framework that improves action-conditioned world models by adding cycle action consistency to the planning cost. It ensures predicted trajectories are physically realizable by checking if an inverse dynamics model's inferred action matches the conditioning action. This consistently enhances planning performance across various tasks and world models.