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

Active Tapping via Gaussian Process for Efficient Unknown Object Surface Reconstruction