  Foundation models are a promising path toward general-purpose and
user-friendly robots. The prevalent approach involves training a generalist
policy that, like a reinforcement learning policy, uses observations to output
actions. Although this approach has seen much success, several concerns arise
when considering deployment and end-user interaction with these systems. In
particular, the lack of modularity between tasks means that when model weights
are updated (e.g., when a user provides feedback), the behavior in other,
unrelated tasks may be affected. This can negatively impact the system's
interpretability and usability. We present an alternative approach to the
design of robot foundation models, Diffusion for Policy Parameters (DPP), which
generates stand-alone, task-specific policies. Since these policies are
detached from the foundation model, they are updated only when a user wants,
either through feedback or personalization, allowing them to gain a high degree
of familiarity with that policy. We demonstrate a proof-of-concept of DPP in
simulation then discuss its limitations and the future of interpretable
foundation models.
