The key factors that determine the performance ceiling of LEO and other embodied generalist agents include:
Model Capacity: The capacity of the model, including the number of parameters and the complexity of the architecture, plays a crucial role in determining the performance ceiling. Increasing the model capacity can lead to better performance up to a certain point.
Data Quality and Quantity: The quality and quantity of the training data significantly impact the model's performance. Having diverse, high-quality data is essential for training robust and generalizable models.
Task Complexity: The complexity of the tasks the model is trained on can also influence its performance ceiling. More complex tasks may require additional capabilities and reasoning skills from the model.
To push the boundaries of embodied generalist agents in the future, the following strategies can be considered:
Advanced Multi-modal Integration: Develop more sophisticated models that can seamlessly integrate vision, language, and action information. This can involve creating more efficient fusion mechanisms and attention mechanisms for multi-modal inputs.
Continual Learning: Implement continual learning frameworks that allow the model to adapt and improve over time with new experiences and data. This can help in addressing limitations and improving performance in real-world scenarios.
Cross-domain Generalization: Enhance the model's ability to generalize across different domains and tasks by exposing it to a wide range of scenarios and environments during training. This can help in building more versatile and adaptable agents.
By focusing on these factors and strategies, researchers can continue to advance the capabilities of embodied generalist agents and push the boundaries of AI in 3D environments.