Google Inc.

Harrison Lynch

Pierre Sermanet

Yevgen Chebotar

Yunfei Bai

Seyed Mohammad Khansari Zadeh

Soeren Pirk

There are provided systems, methods, and apparatus, for optimizing a policy controller to control a robotic agent that interacts with an environment to perform a robotic task. One of the methods includes optimizing the policy controller using a neural network that generates numeric embeddings of images of the environment and a demonstration sequence of demonstration images of another agent performing a version of the robotic task.

Optimizing policy controllers for robotic agents using image embeddings

Training a machine learning model (e.g., a neural network model such as a convolutional neural network (CNN) model) so that, when trained, the model can be utilized in processing vision data (e.g., from a vision component of a robot), that captures an object, to generate a rich object-centric embedding for the vision data. The generated embedding can enable differentiation of even subtle variations of attributes of the object captured by the vision data.

Growing community of inventors

San Francisco, CA, United States of America

Harrison Lynch