3D Grasp Synthesis Based on a Visual Cortex Model

In this paper, the problem of object grasping is considered from both a biological and an engineering point of view. A model of information processing for the grasp synthesis and execution is described based on recent findings from neuroscience. Taking into account the differences between robotic and biological systems, this paper proposes the adaptation of that model to the peculiarities of a robotic system, instead of mimicking it. For this purpose, an architecture is proposed that allows the scalability of this model and its integration within more complex tasks. The grasp synthesis is designed as integrated within the extraction of a 3D object description, so that the object reconstruction is driven by the needs of the grasp synthesis. The integration is formulated as a framework where different grasp synthesis strategies could be applied

[1]  Ravi S. Menon,et al.  Differential Effects of Viewpoint on Object-Driven Activation in Dorsal and Ventral Streams , 2002, Neuron.

[2]  Tomoka Naganuma,et al.  Neural Correlates for Perception of 3D Surface Orientation from Texture Gradient , 2002, Science.

[3]  Eris Chinellato,et al.  Vision and Grasping: Humans vs. Robots , 2005, IWINAC.

[4]  Stephen A. Engel,et al.  Neural Response to Perception of Volume in the Lateral Occipital Complex , 2001, Neuron.

[5]  Michael A. Arbib,et al.  Modeling parietal-premotor interactions in primate control of grasping , 1998, Neural Networks.

[6]  Henrik I. Christensen,et al.  Automatic grasp planning using shape primitives , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[7]  Paulo R. S. Mendonça,et al.  Epipolar geometry from profiles under circular motion , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  M. Goodale,et al.  The visual brain in action , 1995 .

[9]  Kenneth F. Valyear,et al.  A double dissociation between sensitivity to changes in object identity and object orientation in the ventral and dorsal visual streams: A human fMRI study , 2006, Neuropsychologia.

[10]  Vijay Kumar,et al.  Robotic grasping and contact: a review , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[11]  Samia Boukir,et al.  Structure From Controlled Motion , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Gerd Hirzinger,et al.  A fast and robust grasp planner for arbitrary 3D objects , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[13]  Gustavo Deco,et al.  Computational neuroscience of vision , 2002 .

[14]  M. Goodale,et al.  Dual-task interference is greater in delayed grasping than in visually guided grasping. , 2007, Journal of vision.

[15]  M. Arbib,et al.  Grasping objects: the cortical mechanisms of visuomotor transformation , 1995, Trends in Neurosciences.

[16]  B. Dizioglu,et al.  Mechanics of form closure , 1984 .

[17]  Robert B. Fisher,et al.  Visual quality measures for Characterizing Planar robot grasps , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).