  Robust grasping is a major, and still unsolved, problem in robotics.
Information about the 3D shape of an object can be obtained either from prior
knowledge (e.g., accurate models of known objects or approximate models of
familiar objects) or real-time sensing (e.g., partial point clouds of unknown
objects) and can be used to identify good potential grasps. However, due to
modeling and sensing inaccuracies, local exploration is often needed to refine
such grasps and successfully apply them in the real world. The recently
proposed unscented Bayesian optimization technique can make such exploration
safer by selecting grasps that are robust to uncertainty in the input space
(e.g., inaccuracies in the grasp execution). Extending our previous work on 2D
optimization, in this paper we propose a 3D haptic exploration strategy that
combines unscented Bayesian optimization with a novel collision penalty
heuristic to find safe grasps in a very efficient way: while by augmenting the
search-space to 3D we are able to find better grasps, the collision penalty
heuristic allows us to do so without increasing the number of exploration
steps.
