The action masking model may have limitations in capturing the stability of box placements in certain scenarios, such as when there are uncertainties or variations in the physical environment. To improve the robustness of the learned task planner, several strategies can be implemented. Firstly, incorporating feedback mechanisms that provide real-time information on the stability of placements during execution can help refine the action masking model. This feedback loop allows the model to continuously learn and adapt based on the actual outcomes of box placements. Additionally, integrating sensor data from the robotic system to validate the predicted stability of placements can enhance the accuracy of the action masking model. Utilizing advanced simulation techniques that simulate realistic physical conditions and uncertainties can also aid in training the model to handle a wider range of scenarios. By addressing these potential limitations and continuously refining the action masking model, the task planner can become more reliable and robust in real-world applications.