X Development LLC

Google Inc.

Yuchen Wu

Yunfei Bai

Alan S Thompson

Laura Stoia

Gregory Prisament

Elmar Mair

Sarah Coe

Ian Wilkes

Max Moroz

Weidan Wu

Shumeet Baluja

Michele Mae Covell

Josh Sacks

Methods, apparatus, systems, and computer-readable media are provided for optimizing robot-implemented tasks based at least in part on historical task and location correlated duration data collected from one or more robots. Historical task and location correlated duration data may, in some implementations, include durations of different tasks performed in different locations by one or more robots in one or more particular environments, and knowledge of such durations may be used to optimize tasks performed by the same or different robots in the future.

Robot task optimization based on historical task and location correlated durations

Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).

Mitigating reality gap through optimization of simulated hardware parameter(s) of simulated robot

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by one or more non-real-time processors, data defining a light illumination pattern for a robotic device. Generating, by the one or more non-real-time processors and based on the data, a spline that represents the light illumination pattern, where a knot vector of the spline defines a timing profile of the light illumination pattern. Providing the spline to one or more real-time processors of the robotic system. Calculating, by the one or more real-time processors, an illumination value from the spline at each of a plurality of time steps. Controlling, by the one or more real-time processors, illumination of a lighting display of the robotic system in accordance with the illumination value of the spline at each respective time step.

Robotic control

Mitigating the reality gap through utilization of technique(s) that enable compliant robotic control and/or compliant robotic contact to be simulated effectively by a robotic simulator. The technique(s) can include, for example: (1) utilizing a compliant end effector model in simulated episodes of the robotic simulator; (2) using, during the simulated episodes, a soft constraint for a contact constraint of a simulated contact model of the robotic simulator; and/or (3) using proportional derivative (PD) control in generating joint control forces, for simulated joints of the simulated robot, during the simulated episodes. Implementations additionally or alternatively relate to determining parameter(s), for use in one or more of the techniques that enable effective simulation of compliant robotic control and/or compliant robotic contact.

Mitigating reality gap through simulating compliant control and/or compliant contact in robotic simulator

Systems and methods of evaluating information via a computer network are provided. A content group can be identified, and each item of the content group can be associated with a vector indicating at least one user interest category of users exposed to the item. The vectors of each item can be evaluated to generate a first nearest neighbor list of each item of the content group. The nearest neighbor list of a first item can be compared with the nearest neighbor list of a second item. Based on a result of the comparison, the first and second items can be associated in a cluster.

Method and system for evaluating content via a computer network

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San Francisco, CA, United States of America

Yuchen Wu