  Existing object-search approaches enable robots to search through free
pathways, however, robots operating in unstructured human-centered environments
frequently also have to manipulate the environment to their needs. In this
work, we introduce a novel interactive multi-object search task in which a
robot has to open doors to navigate rooms and search inside cabinets and
drawers to find target objects. These new challenges require combining
manipulation and navigation skills in unexplored environments. We present
HIMOS, a hierarchical reinforcement learning approach that learns to compose
exploration, navigation, and manipulation skills. To achieve this, we design an
abstract high-level action space around a semantic map memory and leverage the
explored environment as instance navigation points. We perform extensive
experiments in simulation and the real world that demonstrate that, with
accurate perception, the decision making of HIMOS effectively transfers to new
environments in a zero-shot manner. It shows robustness to unseen subpolicies,
failures in their execution, and different robot kinematics. These capabilities
open the door to a wide range of downstream tasks across embodied AI and
real-world use cases.
