X Development LLC

Google Inc.

Industrial Perception, Inc.

Sri International

Intrinsic Innovation LLC

Neato Robotics, Inc.

Boston Dynamics, Inc.

Kurt Konolige

Ethan Rublee

Stefan Hinterstoisser

Gary R Bradski

Troy Straszheim

Nareshkumar Rajkumar

John William Zevenbergen

Mrinal Kalakrishnan

Hauke Malte Strasdat

Steve Croft

Vincent Louis Dureau

Peter Pastor Sampedro

Adrian Li

Kevin William Watts

Paul Wohlhart

Ali Yahya Valdovinos

Vincent Olivier Vanhoucke

Sergey Levine

Yunfei Bai

Julian MacNeille Mason

Konstantinos Bousmalis

Julian Ibarz

Alexander Irpan

Pankaj Shah

Joe Augenbraun

Joseph Pinzarrone

Hua Tang

Nick Donaldson

Leo Salinas

Rafael Taylor

Steven Derrien

Matthew Laurance Kelcey

Ali Yahya Valdovinoa

Cedric Cagniart

Charles Fiebig

Yuming Liu

Hassaan Moin Khan

Sergey Vladimir Levine

Mrinal Kalakrishanan

Example methods and systems for determining 3D scene geometry by projecting patterns of light onto a scene are provided. In an example method, a first projector may project a first random texture pattern having a first wavelength and a second projector may project a second random texture pattern having a second wavelength. A computing device may receive sensor data that is indicative of an environment as perceived from a first viewpoint of a first optical sensor and a second viewpoint of a second optical sensor. Based on the received sensor data, the computing device may determine corresponding features between sensor data associated with the first viewpoint and sensor data associated with the second viewpoint. And based on the determined corresponding features, the computing device may determine an output including a virtual representation of the environment that includes depth measurements indicative of distances to at least one object.

Determining a virtual representation of an environment by projecting texture patterns

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Generating a model for an object encountered by a robot

Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.

Operating multiple testing robots based on robot instructions and/or environmental parameters received in a request

Methods for annotating objects within image frames are disclosed. Information is obtained that represents a camera pose relative to a scene. The camera pose includes a position and a location of the camera relative to the scene. Data is obtained that represents multiple images, including a first image and a plurality of other images, being captured from different angles by the camera relative to the scene. A 3D pose of the object of interest is identified with respect to the camera pose in at least the first image. A 3D bounding region for the object of interest in the first image is defined, which indicates a volume that includes the object of interest. A location and orientation of the object of interest is determined in the other images based on the defined 3D bounding region of the object of interest and the camera pose in the other images.

Automated data capture

An example method includes receiving first and second images from a first and second imager. The method further includes determining first correlation scores between a target pixel from the first image and respective pixels from the second image based on a first similarity measure. The method also includes identifying a candidate matching pixel to the target pixel based on the first correlation scores. The method further includes determining second correlation scores between the target pixel and each of the candidate matching pixel, a left neighboring pixel, and a right neighboring pixel based on a second similarity measure. The method also includes determining whether the candidate matching pixel is better correlated to the target pixel than each of the neighboring pixels based on the second correlation scores. After determining that the candidate matching pixel is better correlated, the method further includes determining a disparity value by interpolating the second correlation scores.

Disparity interpolation refinement for stereo vision

Example embodiments may relate to methods and systems for selecting a grasp point on an object. In particular, a robotic manipulator may identify characteristics of a physical object within a physical environment. Based on the identified characteristics, the robotic manipulator may determine potential grasp points on the physical object corresponding to points at which a gripper attached to the robotic manipulator is operable to grip the physical object. Subsequently, the robotic manipulator may determine a motion path for the gripper to follow in order to move the physical object to a drop-off location for the physical object and then select a grasp point, from the potential grasp points, based on the determined motion path. After selecting the grasp point, the robotic manipulator may grip the physical object at the selected grasp point with the gripper and move the physical object through the determined motion path to the drop-off location.

Object pickup strategies for a robotic device

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network that is used to control a robotic agent interacting with a real-world environment.

Using simulation and domain adaptation for robotic control

Methods and systems for depth sensing are provided. A system includes a first and second optical sensor each including a first plurality of photodetectors configured to capture visible light interspersed with a second plurality of photodetectors configured to capture infrared light within a particular infrared band. The system also includes a computing device configured to (i) identify first corresponding features of the environment between a first visible light image captured by the first optical sensor and a second visible light image captured by the second optical sensor; (ii) identify second corresponding features of the environment between a first infrared light image captured by the first optical sensor and a second infrared light image captured by the second optical sensor; and (iii) determine a depth estimate for at least one surface in the environment based on the first corresponding features and the second corresponding features.

Imager for detecting visual light and projected patterns

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Menlo Park, CA, United States of America

Kurt Konolige