Papers › Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties
Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties
Jue Kun Li, David Hsu, Wee Sun Lee
This paper introduces Push-Net, a deep recurrent neural network model, which enables a robot to push objects of unknown physical properties for re-positioning and re-orientation, using only visual camera images as input. The unknown physical properties is a major challenge for pushing. Push-Net overcomes the challenge by tracking a history of push interactions with an LSTM module and training an auxiliary objective function that estimates an object’s center of mass. We trained Push-Net entirely in simulation and tested it extensively on many different objects in both simulation and on two real robots, a Fetch arm and a Kinova MICO arm. Experiments suggest that Push-Net is robust and efficient. It achieved over 97% success rate in simulation on average and succeeded in all real robot experiments with a small number of pushes.
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