  Recently developed pretrained models can encode rich world knowledge
expressed in multiple modalities, such as text and images. However, the outputs
of these models cannot be integrated into algorithms to solve sequential
decision-making tasks. We develop an algorithm that utilizes the knowledge from
pretrained models to construct and verify controllers for sequential
decision-making tasks, and to ground these controllers to task environments
through visual observations with formal guarantees. In particular, the
algorithm queries a pretrained model with a user-provided, text-based task
description and uses the model's output to construct an automaton-based
controller that encodes the model's task-relevant knowledge. It allows formal
verification of whether the knowledge encoded in the controller is consistent
with other independently available knowledge, which may include abstract
information on the environment or user-provided specifications. Next, the
algorithm leverages the vision and language capabilities of pretrained models
to link the observations from the task environment to the text-based control
logic from the controller (e.g., actions and conditions that trigger the
actions). We propose a mechanism to provide probabilistic guarantees on whether
the controller satisfies the user-provided specifications under perceptual
uncertainties. We demonstrate the algorithm's ability to construct, verify, and
ground automaton-based controllers through a suite of real-world tasks,
including daily life and robot manipulation tasks.
