Google Inc.

Arkanath Pathak

Abhinav Gupta

Yunfei Bai

Seyed Mohammad Khansari Zadeh

Honglak Lee

James Christopher Davidson

Xinchen Yan

Jasmine Hsu

Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.

Robotic grasping prediction using neural networks and geometry aware object representation

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Darling Harbour, Australia

Arkanath Pathak