  In this paper, we present a method to manipulate unknown objects in-hand
using tactile sensing without relying on a known object model. In many cases,
vision-only approaches may not be feasible; for example, due to occlusion in
cluttered spaces. We address this limitation by introducing a method to
reorient unknown objects using tactile sensing. It incrementally builds a
probabilistic estimate of the object shape and pose during task-driven
manipulation. Our approach uses Bayesian optimization to balance exploration of
the global object shape with efficient task completion. To demonstrate the
effectiveness of our method, we apply it to a simulated Tactile-Enabled Roller
Grasper, a gripper that rolls objects in hand while collecting tactile data. We
evaluate our method on an insertion task with randomly generated objects and
find that it reliably reorients objects while significantly reducing the
exploration time.
