World-Action Model (WAM): An Action-Regularized World Model for Joint Reasoning in Robotics

This paper introduces the World-Action Model (WAM), an innovative action-regularized world model that integrates reasoning about future visual observations with the actions driving state transitions. By incorporating an inverse dynamics objective, WAM significantly enhances policy learning in robotic manipulation tasks, demonstrating improved success rates and training efficiency over existing baselines.