  Mobile Manipulation (MoMa) systems incorporate the benefits of mobility and
dexterity, due to the enlarged space in which they can move and interact with
their environment. However, even when equipped with onboard sensors, e.g., an
embodied camera, extracting task-relevant visual information in unstructured
and cluttered environments, such as households, remains challenging. In this
work, we introduce an active perception pipeline for mobile manipulators to
generate motions that are informative toward manipulation tasks, such as
grasping in unknown, cluttered scenes. Our proposed approach, ActPerMoMa,
generates robot paths in a receding horizon fashion by sampling paths and
computing path-wise utilities. These utilities trade-off maximizing the visual
Information Gain (IG) for scene reconstruction and the task-oriented objective,
e.g., grasp success, by maximizing grasp reachability. We show the efficacy of
our method in simulated experiments with a dual-arm TIAGo++ MoMa robot
performing mobile grasping in cluttered scenes with obstacles. We empirically
analyze the contribution of various utilities and parameters, and compare
against representative baselines both with and without active perception
objectives. Finally, we demonstrate the transfer of our mobile grasping
strategy to the real world, indicating a promising direction for
active-perceptive MoMa.
