Combined shape, appearance and silhouette for simultaneous manipulator and object tracking

By P. Hebert, N. Hudson, Jeremy Ma, T. Howard, Thomas J. Fuchs, M. Bajracharya, Joel W. Burdick. 2012

51 citations recorded by the source corpus.

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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “Comparatively little prior work has addressed direct visual feedback for grasping, most of which requires manually designed features to track the end effector (Vahrenkamp et al., 2008; Hebert et al., 2012).”
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection (2016, background): “Comparatively little prior work has addressed direct visual feedback for grasping, most of which requires manually designed features to track the end effector [Vahrenkamp et al., 2008, Hebert et al., 2012].”
Learning a visuomotor controller for real world robotic grasping using simulated depth images (2017, background): “area [9], only a small amount of previous work addresses using visual feedback directly for grasping [10], [11], [12].”