  Autonomous grasping is an important factor for robots physically interacting
with the environment and executing versatile tasks. However, a universally
applicable, cost-effective, and rapidly deployable autonomous grasping approach
is still limited by those target objects with fuzzy-depth information. Examples
are transparent, specular, flat, and small objects whose depth is difficult to
be accurately sensed. In this work, we present a solution to those fuzzy-depth
objects. The framework of our approach includes two major components: one is a
soft robotic hand and the other one is a Fuzzy-depth Soft Grasping (FSG)
algorithm. The soft hand is replaceable for most existing soft hands/grippers
with body compliance. FSG algorithm exploits both RGB and depth images to
predict grasps while not trying to reconstruct the whole scene. Two grasping
primitives are designed to further increase robustness. The proposed method
outperforms reference baselines in unseen fuzzy-depth objects grasping
experiments (84% success rate).
