Human reach-to-grasp generalization strategies: a Bayesian approach


Human reach-to-grasp generalization strategies: a Bayesian approach


Diego R. Faria [ website | social ]

Ricardo Martins [ website | social ]

Jorge Dias [ website | social ]


This work presents a representation of 3D object shape using a probabilistic volumetric map derived from in-hand exploration. The exploratory procedure is based on contour following through the fingertip movements on the object surface. We first consider the simple case of having single hand exploration of a static object. The cumulative pose data provides a 3D point cloud that is quantized to the probabilistic volumetric map. For each voxel we have a probability distribution for the occupancy percentage. This is then extended to in-hand exploration of non-static objects. Since the object is moving during the in-hand exploration, and we also consider the use of the other hand for re-grasping, object pose has to be tracked. By keeping track of object motion we can register data to the initial pose to build a consistent object representation. An object centered representation is implemented using the computed object center of mass to define its frame of reference. Results are presented for in-hand exploration of both static and non-static objects that show that valid models can be obtained. The 3D object probabilistic representation can be used in several applications related with grasp generation tasks.


Robotics: Science and Systems RSS 2009, Workshop: "Understanding the Human Hand for Advancing Robotic Manipulation"

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robotics, artificial perception, Bayesian modelling, grasping



Human reach-to-grasp generalization strategies: a Bayesian approach