End-to-end dexterous manipulation with deliberate interactive estimation

By N. Hudson, T. Howard, Jeremy Ma, Abhinandan Jain, M. Bajracharya, Steven Myint, Calvin Kuo, Larry H. Matthies, P. Backes, P. Hebert and 2 more. 2012

48 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, background): “Feedback has been incorporated into grasping primarily as a way to achieve the desired forces for force closure and other dynamic grasping criteria (Hudson et al., 2012), as well as in the form of standard servoing mechanisms, including visual servoing (described below) to servo the gripper to a…”
Data-Driven Grasp Synthesis—A Survey (2013, background): “There are a number of approaches that use constant tactile or visual feedback during grasp execution to adapt to unforeseen situations [23, 26, 25, 137, 138, 139, 123].”
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection (2016, unknown): “Feedback has been incorporated into grasping primarily as a way to achieve the desired forces for force closure and other dynamic grasping criteria [Hudson et al., 2012], as well as in the form of standard servoing mechanisms, including visual servoing (described below) to servo the gripper to a…”
Autonomously Acquiring Instance-Based Object Models from Experience (2015, background): “[10] used active perception to create a grasping system capable of carrying out a variety of complex tasks.”