Efficient robotic grasping using simulation and domain adaptation.

Other literature type English OPEN
Kelcey, Matthew;
  • Identifiers: doi: 10.4225/03/5abc50a4374ab
  • Subject: Artificial Intelligence and Image Processing | Control Systems, Robotics and Automation | Machine Learning Models | Monash eResearch Machine Learning Symposium 2018

Data collection for training robotic grasping controllers is expensive in both time and price. Methods for making use of simulated data are very appealing as they reduce this expense dramatically, but often fail to generalise to a real world environment. GraspGAN is a a... View more
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