X Development LLC

Google Inc.

Adrian Ling Hin Li

Mrinal Kalakrishnan

Nicolas Hudson

Sergey Levine

Yevgen Chebotar

Ali Hamid Yahya Valdovinos

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for predicting object pose. In one aspect, a method includes receiving an image of an object having one or more feature points; providing the image as an input to a neural network subsystem trained to receive images of objects and to generate an output including a heat map for each feature point; applying a differentiable transformation on each heat map to generate respective one or more feature coordinates for each feature point; providing the feature coordinates for each feature point as input to an object pose solver configured to compute a predicted object pose for the object, wherein the predicted object pose for the object specifies a position and an orientation of an object; and receiving, at the output of the object pose solver, a predicted object pose for the object in the image.

Object pose neural network system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing an instance of the robotic task for multiple local workers, generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task, optimizing a local policy controller on the trajectory, generating an optimized trajectory using the optimized local controller, and storing the optimized trajectory in a replay memory associated with the local worker. The method includes sampling, for multiple global workers, an optimized trajectory from one of one or more replay memories associated with the global worker, and training the replica of the global policy neural network maintained by the global worker on the sampled optimized trajectory to determine delta values for the parameters of the global policy neural network.

Control policies for collective robot learning

Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing a plurality of instances of the robotic task. For each instance of the robotic task, the method includes generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task in accordance with current values of the parameters of the global policy neural network, and optimizing a local policy controller that is specific to the instance on the trajectory of state-action pairs for the instance. The method further includes generating training data for the global policy neural network using the local policy controllers, and training the global policy neural network on the training data to adjust the current values of the parameters of the global policy neural network.

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San Francisco, CA, United States of America

Adrian Ling Hin Li