  Human ability of both versatile grasping of given objects and grasping of
novel (as of yet unseen) objects is truly remarkable. This probably arises from
the experience infants gather by actively playing around with diverse objects.
Moreover, knowledge acquired during this process is reused during learning of
how to grasp novel objects. We conjecture that this combined process of active
and transfer learning boils down to a random search around an object, suitably
biased by prior experience, to identify promising grasps. In this paper we
present an active learning method for learning of grasps for given objects, and
a transfer learning method for learning of grasps for novel objects. Our
learning methods apply a kernel adaptive Metropolis-Hastings sampler that
learns an approximation of the grasps' probability density of an object while
drawing grasp proposals from it. The sampler employs simulated annealing to
search for globally-optimal grasps. Our empirical results show promising
applicability of our proposed learning schemes.
