  Robotic grasping traditionally relies on object features or shape information
for learning new or applying already learned grasps. We argue however that such
a strong reliance on object geometric information renders grasping and grasp
learning a difficult task in the event of cluttered environments with high
uncertainty where reasonable object models are not available. This being so, in
this paper we thus investigate the application of model-free stochastic
optimization for grasp learning. For this, our proposed learning method
requires just a handful of user-demonstrated grasps and an initial prior by a
rough sketch of an object's grasp affordance density, yet no object geometric
knowledge except for its pose. Our experiments show promising applicability of
our proposed learning method.
