Visual servoing for humanoid grasping and manipulation tasks
By Nikolaus Vahrenkamp, Steven Wieland, Pedram Azad, David Israel Gonzalez-Aguirre, Tamim Asfour, Rüdiger Dillmann. 2008
92 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Sergey Levine
- Peter Pastor
- Deirdre Quillen
- Jeannette Bohg
- Kate Saenko
- Stefan Schaal
- Douglas Morrison
- Peter Corke
In Computer Vision and Pattern Recognition
- Sergey Levine
- Abhinav Kumar Gupta
- Luc Van Gool
- Trevor Darrell
- Jitendra Malik
- Andrew Zisserman
- Dacheng Tao
- Andrea Vedaldi
In Control and Systems Engineering
- Sergey Levine
- Peter Pastor
- Pieter Abbeel
- Chelsea Finn
- Jürgen Leitner
- Peter Corke
- Deirdre Quillen
- Douglas Morrison
Citations in the corpus
- 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).”
- Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach (2018, background): “However, the nature of visual servoing methods mean that they typically rely on hand-crafted image features for object detection [13, 30] or object pose estimation [11], so do not perform any online grasp synthesis but instead converge to a pre-determined goal pose and are not applicable to unknown…”
- 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].”
- 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 robust, real-time, reactive robotic grasping (2019, background): “However, the nature of visual servoing methods mean that they typically rely on hand-crafted image features for object detection (Kober et al., 2012; Vahrenkamp et al., 2008) or object pose estimation (Horaud et al., 1998), so do not perform any online grasp synthesis but instead converge to a…”
- Robot arm pose estimation by pixel-wise regression of joint angles (2016, background): “Often markers are attached to the arm to simplify detection [4], [5].”