  Inspired by widely used soft fingers on grasping, we propose a method of
rigid-soft interactive learning, aiming at reducing the time of data
collection. In this paper, we classify the interaction categories into
Rigid-Rigid, Rigid-Soft, Soft-Rigid according to the interaction surface
between grippers and target objects. We find experimental evidence that the
interaction types between grippers and target objects play an essential role in
the learning methods. We use soft, stuffed toys for training, instead of
everyday objects, to reduce the integration complexity and computational burden
and exploit such rigid-soft interaction by changing the gripper fingers to the
soft ones when dealing with rigid, daily-life items such as the
Yale-CMU-Berkeley (YCB) objects. With a small data collection of 5K picking
attempts in total, our results suggest that such Rigid-Soft and Soft-Rigid
interactions are transferable. Moreover, the combination of different grasp
types shows better performance on the grasping test. We achieve the best
grasping performance at 97.5\% for easy YCB objects and 81.3\% for difficult
YCB objects while using a precise grasp with a two-soft-finger gripper to
collect training data and power grasp with a four-soft-finger gripper to test.
