Bridging the Reality Gap: Improving World Models for AI Planning

The research focuses on addressing the common issue of performance degradation when deploying AI planning models from simulation (training) to the real world (testing). It likely explores techniques to make the simulated environment a more accurate reflection of reality, thereby improving generalizability.

• •Addresses the challenge of the train-test gap in AI planning.
• •Likely investigates methods to improve world model accuracy and realism.
• •Potentially focuses on gradient-based planning methods.