Active tactile object exploration with Gaussian processes

Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy...

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Bibliographic Details
Main Authors: Zhengkun Yi, Calandra, Roberto, Veiga, Filipe, van Hoof, Herke, Hermans, Tucker, Yilei Zhang, Peters, Jan
Format: Conference Proceeding
Language:English
Subjects:
Gaussian processes
Robot sensing systems
Shape
Surface impedance
Surface reconstruction
Surface treatment
Online Access:Request full text
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Description
Summary:Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy to efficiently reduce the surface geometry uncertainty by leveraging a probabilistic representation of object surface. In particular, we model the object surface using a Gaussian process and use the associated uncertainty information to efficiently determine the next point to explore. We validate the resulting method for tactile object surface modeling using a real robot to reconstruct multiple, complex object surfaces.
ISSN:2153-0866
DOI:10.1109/IROS.2016.7759723