  Building a general-purpose intelligent home-assistant agent skilled in
diverse tasks by human commands is a long-term blueprint of embodied AI
research, which poses requirements on task planning, environment modeling, and
object interaction. In this work, we study primitive mobile manipulations for
embodied agents, i.e. how to navigate and interact based on an instructed
verb-noun pair. We propose DISCO, which features non-trivial advancements in
contextualized scene modeling and efficient controls. In particular, DISCO
incorporates differentiable scene representations of rich semantics in object
and affordance, which is dynamically learned on the fly and facilitates
navigation planning. Besides, we propose dual-level coarse-to-fine action
controls leveraging both global and local cues to accomplish mobile
manipulation tasks efficiently. DISCO easily integrates into embodied tasks
such as embodied instruction following. To validate our approach, we take the
ALFRED benchmark of large-scale long-horizon vision-language navigation and
interaction tasks as a test bed. In extensive experiments, we make
comprehensive evaluations and demonstrate that DISCO outperforms the art by a
sizable +8.6% success rate margin in unseen scenes, even without step-by-step
instructions. Our code is publicly released at
https://github.com/AllenXuuu/DISCO.
