  Object surface reconstruction brings essential benefits to robot grasping,
object recognition, and object manipulation. When measuring the surface
distribution of an unknown object by tapping, the greatest challenge is to
select tapping positions efficiently and accurately without prior knowledge of
object region. Given a searching range, we propose an active exploration
method, to efficiently and intelligently guide the tapping to learn the object
surface without exhaustive and unnecessary off-surface tapping. We analyze the
performance of our approach in modeling object surfaces within an exploration
range larger than the object using a robot arm equipped with an end-of-arm
tapping tool to execute tapping motions. Experimental results show that the
approach successfully models the surface of unknown objects with a relative 59%
improvement in the proportion of necessary taps among all taps compared with
state-of-art performance.
