While the QueST architecture demonstrates significant advancements in learning sharable skills, it does have limitations that can be addressed to enhance its applicability to more diverse and open-ended task distributions.
Task Similarity: The current architecture primarily excels in environments where tasks are structurally similar to those seen during training. This reliance on task similarity may hinder performance in scenarios with highly diverse or novel tasks. To address this, QueST could be extended by incorporating meta-learning techniques that enable the model to adapt quickly to new tasks with minimal data.
Scalability of the Codebook: The fixed size of the discrete latent space may limit the model's ability to capture a wide range of motion primitives, especially in complex environments. Expanding the codebook size or employing a dynamic codebook that can grow based on the diversity of tasks encountered could enhance the model's representational capacity.
Incorporation of Contextual Information: The current QueST architecture does not fully leverage contextual information beyond the immediate observations and task descriptions. Integrating additional contextual cues, such as environmental states or user preferences, could improve the model's ability to generalize across tasks and adapt to varying conditions.
Exploration of Inductive Biases: The architecture currently focuses on causality as an inductive bias. Future iterations could explore other biases, such as geometric invariance or temporal consistency, to enhance the model's robustness in learning and executing tasks in dynamic environments.
Open-Ended Learning: To handle open-ended task distributions, QueST could be augmented with reinforcement learning techniques that allow the model to continuously learn from interactions with the environment. This would enable the robot to refine its skills and adapt to new tasks as they arise, fostering a more flexible and autonomous learning process.