The potential limitations of the current self-explainable embodied caption generation approach can be addressed and improved in the following ways:
Diverse Training Data: Increasing the diversity of training data, including a wider range of object-action interactions and scenarios, can help the model generalize better and improve its interpretability across different robotic platforms. This can involve collecting data from various sources and environments to ensure robust performance.
Fine-tuning and Adaptation: Implementing fine-tuning techniques and adaptive learning strategies can help the model adjust to different robotic platforms and environments. By continuously updating the model with new data and feedback, it can become more flexible and adaptable to varying conditions.
Human Feedback Integration: Incorporating mechanisms for human feedback and correction can enhance the interpretability of the model. By allowing humans to provide input and guidance, the model can learn from real-world interactions and improve its performance in diverse settings.
Interpretability Metrics: Developing specific metrics to evaluate the interpretability of the generated captions can provide insights into the model's performance. By measuring the clarity, relevance, and accuracy of the embodied captions, improvements can be made to enhance interpretability for a wider range of robotic platforms.