  Robots operating in the real world require both rich manipulation skills as
well as the ability to semantically reason about when to apply those skills.
Towards this goal, recent works have integrated semantic representations from
large-scale pretrained vision-language (VL) models into manipulation models,
imparting them with more general reasoning capabilities. However, we show that
the conventional pretraining-finetuning pipeline for integrating such
representations entangles the learning of domain-specific action information
and domain-general visual information, leading to less data-efficient training
and poor generalization to unseen objects and tasks. To this end, we propose
ProgramPort, a modular approach to better leverage pretrained VL models by
exploiting the syntactic and semantic structures of language instructions. Our
framework uses a semantic parser to recover an executable program, composed of
functional modules grounded on vision and action across different modalities.
Each functional module is realized as a combination of deterministic
computation and learnable neural networks. Program execution produces
parameters to general manipulation primitives for a robotic end-effector. The
entire modular network can be trained with end-to-end imitation learning
objectives. Experiments show that our model successfully disentangles action
and perception, translating to improved zero-shot and compositional
generalization in a variety of manipulation behaviors. Project webpage at:
\url{https://progport.github.io}.
