  Grasping objects under uncertainty remains an open problem in robotics
research. This uncertainty is often due to noisy or partial observations of the
object pose or shape. To enable a robot to react appropriately to unforeseen
effects, it is crucial that it continuously takes sensor feedback into account.
While visual feedback is important for inferring a grasp pose and reaching for
an object, contact feedback offers valuable information during manipulation and
grasp acquisition. In this paper, we use model-free deep reinforcement learning
to synthesize control policies that exploit contact sensing to generate robust
grasping under uncertainty. We demonstrate our approach on a multi-fingered
hand that exhibits more complex finger coordination than the commonly used
two-fingered grippers. We conduct extensive experiments in order to assess the
performance of the learned policies, with and without contact sensing. While it
is possible to learn grasping policies without contact sensing, our results
suggest that contact feedback allows for a significant improvement of grasping
robustness under object pose uncertainty and for objects with a complex shape.
