  A kitchen assistant needs to operate human-scale objects, such as cabinets
and ovens, in unmapped environments with dynamic obstacles. Autonomous
interactions in such real-world 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. This limitation is in part due to two main reasons: 1) inability to
interact with unknown human-scale objects such as cabinets and ovens, and 2)
inefficient coordination between the arm and the mobile base. 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 uses a
learned model to estimate the articulated model of a target object from an
RGB-D input and predicts an action-conditional sequence of states for
interaction. The second stage comprises of a whole-body motion controller to
manipulate the object along the generated kinematic plan. We show that our
proposed pipeline can handle complicated static and dynamic kitchen settings.
Moreover, we demonstrate that the proposed approach achieves better performance
than commonly used control methods in mobile manipulation. For additional
material, please check: https://www.pair.toronto.edu/articulated-mm/ .
