  A successful grasp requires careful balancing of the contact forces. Deducing
whether a particular grasp will be successful from indirect measurements, such
as vision, is therefore quite challenging, and direct sensing of contacts
through touch sensing provides an appealing avenue toward more successful and
consistent robotic grasping. However, in order to fully evaluate the value of
touch sensing for grasp outcome prediction, we must understand how touch
sensing can influence outcome prediction accuracy when combined with other
modalities. Doing so using conventional model-based techniques is exceptionally
difficult. In this work, we investigate the question of whether touch sensing
aids in predicting grasp outcomes within a multimodal sensing framework that
combines vision and touch. To that end, we collected more than 9,000 grasping
trials using a two-finger gripper equipped with GelSight high-resolution
tactile sensors on each finger, and evaluated visuo-tactile deep neural network
models to directly predict grasp outcomes from either modality individually,
and from both modalities together. Our experimental results indicate that
incorporating tactile readings substantially improve grasping performance.
