  Suction cups offer a useful gripping solution, particularly in industrial
robotics and warehouse applications. Vision-based grasp algorithms, like
Dex-Net, show promise but struggle to accurately perceive dark or reflective
objects, sub-resolution features, and occlusions, resulting in suction cup grip
failures. In our prior work, we designed the Smart Suction Cup, which estimates
the flow state within the cup and provides a mechanically resilient
end-effector that can inform arm feedback control through a sense of touch. We
then demonstrated how this cup's signals enable haptically-driven search
behaviors for better grasping points on adversarial objects. This prior work
uses a model-based approach to predict the desired motion direction, which
opens up the question: does a data-driven approach perform better? This
technical report provides an initial analysis harnessing the data previously
collected. Specifically, we compare the model-based method with a preliminary
data-driven approach to accurately estimate lateral pose adjustment direction
for improved grasp success.
