  A kitchen assistant needs to operate human-scale objects, such as cabinets
and ovens, in unmapped environments with dynamic obstacles. Autonomous
interactions in such environments require integrating dexterous manipulation
and fluid mobility. While mobile manipulators in different form factors provide
an extended workspace, their real-world adoption has been limited. Executing a
high-level task for general objects requires a perceptual understanding of the
object as well as adaptive whole-body control among dynamic obstacles. In this
paper, we propose a two-stage architecture for autonomous interaction with
large articulated objects in unknown environments. The first stage,
object-centric planner, only focuses on the object to provide an
action-conditional sequence of states for manipulation using RGB-D data. The
second stage, agent-centric planner, formulates the whole-body motion control
as an optimal control problem that ensures safe tracking of the generated plan,
even in scenes with moving obstacles. We show that the proposed pipeline can
handle complex static and dynamic kitchen settings for both wheel-based and
legged mobile manipulators. Compared to other agent-centric planners, our
proposed planner achieves a higher success rate and a lower execution time. We
also perform hardware tests on a legged mobile manipulator to interact with
various articulated objects in a kitchen. For additional material, please
check: www.pair.toronto.edu/articulated-mm/.
