While memory enhancement plays a crucial role in improving performance for navigation tasks like Object Navigation (ObjNav) and Portable Object Navigation (P-ObjectNav), there are some drawbacks and limitations to consider:
Overfitting: Relying too heavily on memory may lead to overfitting if the agent memorizes specific patterns rather than generalizing well across different scenarios.
Computational Complexity: Memory-enhanced models often require more computational resources due to storing past observations and decisions, potentially leading to increased training times and inference latency.
Limited Generalization: Depending excessively on historical data stored in memory may limit an agent's ability to adapt quickly to new or unseen situations that deviate from learned patterns.
Memory Management: Managing large amounts of historical data stored in memory trees or buffers can become challenging, especially when dealing with long sequences of observations.
Interference between Memories: When multiple memories compete for attention during decision-making processes, conflicts may arise that hinder optimal performance.