  Suction cup grasping is very common in industry, but moving too quickly can
cause suction cups to detach, causing drops or damage. Maintaining a suction
grasp throughout a high-speed motion requires balancing suction forces against
inertial forces while the suction cups deform under strain. In this paper, we
consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an
algorithm that combines deep learning with optimization to decrease transport
time while avoiding suction cup failure. GOMP-ST first repeatedly moves a
physical robot, vacuum gripper, and a sample object, while measuring pressure
with a solid-state sensor to learn critical failure conditions. Then, these are
integrated as constraints on the accelerations at the end-effector into a
time-optimizing motion planner. The resulting plans incorporate real-world
effects such as suction cup deformation that are difficult to model
analytically. In GOMP-ST, the learned constraint, modeled with a neural
network, is linearized using Autograd and integrated into a sequential
quadratic program optimization. In 420 experiments with a physical UR5
transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline
optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup
failure while decreasing transport times from 16% to 58%. For code, video, and
datasets, see https://sites.google.com/view/gomp-st.
