Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning
By Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David V. Gealy, Ken Goldberg. 2018
244 citations recorded by the source corpus.
Subfields: Biomedical Engineering, 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 Biomedical Engineering
- Sergey Levine
- Chelsea Finn
- Julian Ibarz
- Mrinal Kalakrishnan
- Peter Pastor
- Ken Goldberg
- Jie Tan
- Pieter Abbeel
In Control and Systems Engineering
- Sergey Levine
- Chelsea Finn
- Pieter Abbeel
- Mrinal Kalakrishnan
- Julian Ibarz
- Peter Pastor
- Jie Tan
- Ken Goldberg
Citations in the corpus
- Learning ambidextrous robot grasping policies (2019, methodology): “The policy optimizes for the highest-qualitygraspforeachgripperseparately,usingthecross-entropymethod (CEM)( 10 , 19 , 27 , 56 ), and then selects the grasps with the highest estimated quality across the grippers.”
- How to train your robot with deep reinforcement learning: lessons we have learned (2021, background): “Finally, in contrast to many current grasping systems that utilize depth sensing (Mahler et al. 2018; Morrison et al. 2018) or wrist-mounted cameras (Viereck et al. 2017; Morrison et al. 2018), QT-Opt operates on raw monocular RGB observations from an over-the-shoulder camera that doesn’t need to…”