  Suction cups are an important gripper type in industrial robot applications,
and prior literature focuses on using vision-based planners to improve grasping
success in these tasks. Vision-based planners can fail due to adversarial
objects or lose generalizability for unseen scenarios, without retraining
learned algorithms. We propose haptic exploration to improve suction cup
grasping when visual grasp planners fail. We present the Smart Suction Cup, an
end-effector that utilizes internal flow measurements for tactile sensing. We
show that model-based haptic search methods, guided by these flow measurements,
improve grasping success by up to 2.5x as compared with using only a vision
planner during a bin-picking task. In characterizing the Smart Suction Cup on
both geometric edges and curves, we find that flow rate can accurately predict
the ideal motion direction even with large postural errors. The Smart Suction
Cup includes no electronics on the cup itself, such that the design is easy to
fabricate and haptic exploration does not damage the sensor. This work
motivates the use of suction cups with autonomous haptic search capabilities in
especially adversarial scenarios.
