X Development LLC

Google Inc.

Clover Network, Inc.

First Data Corporation

Michael Joseph Quinlan

Sean Kirmani

Leonard Robert Speiser

John Daniel Beatty

Guy Satat

Tamer Mohamed El Calamawy

Gabriel Aaron Cohen

Angelique Julie Lausier

Anelia Angelova

John D Beatty

Jacob Whitaker Abrams

Ariel Gordon

Jeffrey Blattman

Sarah Coe

Arvin C Haywood

Jacques Grove

A method includes receiving, from a camera disposed on a robotic device, a two-dimensional (2D) image of a body of an actor and determining, for each respective keypoint of a first subset of a plurality of keypoints, 2D coordinates of the respective keypoint within the 2D image. The plurality of keypoints represent body locations. Each respective keypoint of the first subset is visible in the 2D image. The method also includes determining a second subset of the plurality of keypoints. Each respective keypoint of the second subset is not visible in the 2D image. The method further includes determining, by way of a machine learning model, an extent of engagement of the actor with the robotic device based on (i) the 2D coordinates of keypoints of the first subset and (ii) for each respective keypoint of the second subset, an indicator that the respective keypoint is not visible.

Engagement detection and attention estimation for human-robot interaction

Methods, systems, and apparatus for receiving a command for controlling a robot, the command referencing an object, receiving sensor data for a portion of an environment of the robot, identifying, from the sensor data, a gesture of a human that indicates a spatial region located outside of the portion of the environment described by the sensor data, searching map data for the object, determining, based at least on searching the map data for the object referenced in the command, that the object referenced in the command is present in the spatial region, and in response to determining that the object referenced in the command is present in the spatial region, controlling the robot to perform an action with respect to the object referenced in the command.

Multimodal object identification

A method includes receiving image data representing an environment of a robotic device from a camera on the robotic device. The method further includes applying a trained dense network to the image data to generate a set of feature values, where the trained dense network has been trained to accomplish a first robot vision task. The method additionally includes applying a trained task-specific head to the set of feature values to generate a task-specific output to accomplish a second robot vision task, where the trained task-specific head has been trained to accomplish the second robot vision task based on previously generated feature values from the trained dense network, where the second robot vision task is different from the first robot vision task. The method also includes controlling the robotic device to operate in the environment based on the task-specific output generated to accomplish the second robot vision task.

Shared dense network with robot task-specific heads

This disclosure describes systems, methods, and computer-readable media related to providing a point of sale platform. In some embodiment, a point of sale (POS) device may receive information associated with an order and payment information associated with the order. The POS device may generate a first object based at least in part on the information associated with the order and a second object based at least in part on the payment information. The POS device may store the first object and the second object in a queue. The POS device may transmit the first object and the second object to a remote server.

Systems and methods for providing a point of sale platform

A method includes receiving a first depth map that includes a plurality of first pixel depths and a second depth map that includes a plurality of second pixel depths. The first depth map corresponds to a reference depth scale and the second depth map corresponds to a relative depth scale. The method includes aligning the second pixel depths with the first pixel depths. The method includes transforming the aligned region of the second pixel depths such that transformed second edge pixel depths of the aligned region are coextensive with first edge pixel depths surrounding the corresponding region of the first pixel depths. The method includes generating a third depth map. The third depth map includes a first region corresponding to the first pixel depths and a second region corresponding to the transformed and aligned region of the second pixel depths.

Fusing multiple depth sensing modalities

A method includes receiving sensor data representing a first object in an environment and generating, based on the sensor data, a first state vector that represents physical properties of the first object. The method also includes generating, by a first machine learning model and based on the first state vector and a second state vector that represents physical properties of a second object previously observed in the environment, a metric indicating a likelihood that the first object is the same as the second object. The method further includes determining, based on the metric, to update the second state vector and updating, by a second machine learning model configured to maintain the second state vector over time and based on the first state vector, the second state vector to incorporate into the second state vector information concerning physical properties of the second object as represented in the first state vector.

Object association using machine learning models

A method includes receiving sensor data from a plurality of robot sensors on a robot. The method includes generating a depth map that includes a plurality of pixel depths. The method includes determining, for each respective pixel depth, based on the at least one robot sensor associated with the respective pixel depth, a pixelwise confidence level indicative of a likelihood that the respective pixel depth accurately represents a distance between the robot and a feature of the environment. The method includes generating a pixelwise filterable depth map for a control system of the robot. The pixelwise filterable depth map is filterable to produce a robot operation specific depth map. The robot operation specific depth map is determined based on a comparison of each respective pixelwise confidence level with a confidence threshold corresponding to at least one operation of the robot controlled by the control system of the robot.

Pixelwise filterable depth maps for robots

Systems and methods are provided for adjusting point of sale (POS) interfaces. In some embodiments, a POS device includes a display. The display is configured to present a user interface, the user interface having a first interface and a second interface. The first interface has a first set of functionalities that is prevented from being accessible in the second interface.

Systems and methods for adjusting point-of-sale interfaces

A computer-implemented method for an extensible point-of-sale device is disclosed. The method includes registering a third-party application to be notified of a transaction change on the point-of-sale device. The method also includes displaying a user interface to a user during a purchase transaction on a display of the point-of-sale device using one of a register module and a payment module. The method also includes receiving the transaction change via the user interface of the point-of-sale device. The method also includes broadcasting the transaction change to a set of registered applications that includes the third-party application. The method also includes taking an action that modifies the purchase transaction using the third-party application in response to the broadcasting.

Growing community of inventors

Sunnyvale, CA, United States of America

Michael Joseph Quinlan