In this work, we provide a solution for posturing the anthropomorphic Robonaut-2 hand and arm for grasping based on visual information. A mapping between visual features extracted from a convolutional neural network (CNN) to grasp points is learned. We demonstrate that a pre-trained CNN for image classification can be applied to a grasping task based on a small set of grasping examples. Our approach takes advantage of the hierarchical nature of the CNN and identifies the 3D positions of features that capture the hierarchical support relations between filters in different CNN layers by tracing the activation of higher level features in the CNN backward. When this backward trace terminates in the RGB-D image, important manipulable structures comprising the objects are, thus, localized. These features located in different layers of the CNN are then associated to controllers belonging to different hierarchies of the robot morphology for grasping. A Grasping Dataset is collected using demonstrated hand object relationships for Robonaut-2 to evaluate the proposed approach in terms of the precision of the resulting preshape postures. We demonstrate that this approach outperforms base-line approaches in cluttered scenarios on the Grasping Dataset and a point cloud based approach on a grasping task using Robonaut-2.
Prologue 1. A tragic accident 2. Doing without seeing 3. When vision for action fails 4. The origins of vision: from modules to models 5. Streams within streams 6. Why do we need two systems? 7. Getting it all together 8. Postscript: Dee's life 15 years on Epilogue Abstract Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that permit identification and recognition may be computed independently of the set of descriptions that allow an observer to shape the hand appropriately to pick up an object. We propose that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the striate cortex to the posterior pariet al region mediates the required sensorimotor transformations for visually guided actions directed at such objects. Abstract Evidence is reviewed indicating that striate cortex in the monkey is the source of two multisynaptic corticocortical pathways. One courses ventrally, interconnecting the striate, prestriate, and inferior temporal areas, and enables the visual identification of objects. The other runs dorsally, interconnecting the striate, prestriate, and inferior pariet al areas, and allows instead the visual location of objects. How the information carried in these two separate pathways is reintegrated has become an important question for future research. 