  When adopting a deep learning model for embodied agents, it is required that
the model structure be optimized for specific tasks and operational conditions.
Such optimization can be static such as model compression or dynamic such as
adaptive inference. Yet, these techniques have not been fully investigated for
embodied control systems subject to time constraints, which necessitate
sequential decision-making for multiple tasks, each with distinct inference
latency limitations. In this paper, we present MoDeC, a time constraint-aware
embodied control framework using the modular model adaptation. We formulate
model adaptation to varying operational conditions on resource and time
restrictions as dynamic routing on a modular network, incorporating these
conditions as part of multi-task objectives. Our evaluation across several
vision-based embodied environments demonstrates the robustness of MoDeC,
showing that it outperforms other model adaptation methods in both performance
and adherence to time constraints in robotic manipulation and autonomous
driving applications
