Google Inc.

James Christopher Davidson

Abhinav Gupta

Yunfei Bai

Seyed Mohammad Khansari Zadeh

Honglak Lee

Xinchen Yan

Jasmine Hsu

Arkanath Pathak

Anna Lynn Patterson

Sergey Levine

Rahul Sukthankar

Advay Mengle

Ertan Dogrultan

Saurabh Gupta

Jitendra Malik

Sergey Vladimir Levine

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

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for navigation using visual inputs. One of the systems includes a mapping subsystem configured to, at each time step of a plurality of time steps, generate a characterization of an environment from an image of the environment at the time step, wherein the characterization comprises an environment map identifying locations in the environment having a particular characteristic, and wherein generating the characterization comprises, for each time step: obtaining the image of the environment at the time step, processing the image to generate a first initial characterization for the time step, obtaining a final characterization for a previous time step, processing the characterization for the previous time step to generate a second initial characterization for the time step, and combining the first initial characterization and the second initial characterization to generate a final characterization for the time step.

Agent navigation using visual inputs

Methods and apparatus related to automatically rewriting a string of text utilizing one or more rewrite rules. Some implementations are directed to scoring rewrite rules based at least in part on user interactions with rewrites that are generated by applying the rewrite rules. Some implementations are directed to determining the effectiveness of a rewrite generated based on applying one or more rewrite rules to a string of text. In some of those implementations, the determination may be based at least in part on one or more characteristics of the string of text, one or more characteristics of the rewrite, and/or scores associated with the rewrite rules.

Methods and apparatus related to automatically rewriting strings of text

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Alameda, CA, United States of America

James Christopher Davidson