  Recent advancements in object shape completion have enabled impressive object
reconstructions using only visual input. However, due to self-occlusion, the
reconstructions have high uncertainty in the occluded object parts, which
negatively impacts the performance of downstream robotic tasks such as
grasping. In this work, we propose an active visuo-haptic shape completion
method called Act-VH that actively computes where to touch the objects based on
the reconstruction uncertainty. Act-VH reconstructs objects from point clouds
and calculates the reconstruction uncertainty using IGR, a recent
state-of-the-art implicit surface deep neural network. We experimentally
evaluate the reconstruction accuracy of Act-VH against five baselines in
simulation and in the real world. We also propose a new simulation environment
for this purpose. The results show that Act-VH outperforms all baselines and
that an uncertainty-driven haptic exploration policy leads to higher
reconstruction accuracy than a random policy and a policy driven by Gaussian
Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the
best reconstruction baseline on grasping 10 novel objects. The results show
that Act-VH reaches a significantly higher grasp success rate than the baseline
on all objects. Together, this work opens up the door for using active
visuo-haptic shape completion in more complex cluttered scenes.
