X Development LLC

Google Inc.

Industrial Perception, Inc.

Matterport, Inc.

Intrinsic Innovation LLC

Arraiy, Inc.

Boston Dynamics, Inc.

Ethan Rublee

Gary R Bradski

Kurt Konolige

John William Zevenbergen

Troy Straszheim

Hauke Malte Strasdat

Stefan Hinterstoisser

Steve Croft

Stefan Nusser

William H Nguyen

Mona Fathollahi

Mrinal Kalakrishnan

Kevin William Watts

Gholamreza Amayeh

Grace Vesom

Kyle Lutz

Daniel A Aden

Moshe Benezra

Jared Stephen Russell

Robert Holmberg

Robert Jason Rose

Michael Tetelman

Ian Meeder

Varsha Vivek

Mrinal Kalakrishanan

Systems and methods are provided for specifying safety rules for robotic devices. A computing device can determine information about any actors present within a predetermined area of an environment. The computing device can determine a safety classification for the predetermined area based on the information. The safety classification can include: a low safety classification if the information indicates zero actors are present within the predetermined area, a medium safety classification if the information indicates any actors are present within the predetermined area all are of a predetermined first type, and a high safety classification if the information indicates at least one actor present within the predetermined area is of a predetermined second type. After determining the safety classification for the predetermined area, the computing device can provide a safety rule for operating within the predetermined area to a robotic device operating in the environment.

Safety system for integrated human/robotic environments

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

Systems and methods for registering arbitrary visual features for use as fiducial elements are disclosed. An example method includes aligning a geometric reference object and a visual feature and capturing an image of the reference object and feature. The method also includes identifying, in the image of the object and the visual feature, a set of at least four non-colinear feature points in the visual feature. The method also includes deriving, from the image, a coordinate system using the geometric object. The method also comprises providing a set of measures to each of the points in the set of at least four non-colinear feature points using the coordinate system. The measures can then be saved in a memory to represent the registered visual feature and serve as the basis for using the registered visual feature as a fiducial element.

Arbitrary visual features as fiducial elements

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 and systems regarding importance sampling for the modification of a training procedure used to train a segmentation network are disclosed herein. A disclosed method includes segmenting an image using a trainable directed graph to generate a segmentation, displaying the segmentation, receiving a first selection directed to the segmentation, and modifying a training procedure for the trainable directed graph using the first selection. In a more specific method, the training procedure alters a set of trainable values associated with the trainable directed graph based on a delta between the segmentation and a ground truth segmentation, the first selection is spatially indicative with respect to the segmentation, and the delta is calculated based on the first selection.

Importance sampling for segmentation network training modification

A trained network for point tracking includes an input layer configured to receive an encoding of an image. The image is of a locale or object on which the network has been trained. The network also includes a set of internal weights which encode information associated with the locale or object, and a tracked point therein or thereon. The network also includes an output layer configured to provide an output based on the image as received at the input layer and the set of internal weights. The output layer includes a point tracking node that tracks the tracked point in the image. The point tracking node can track the point by generating coordinates for the tracked point in an input image of the locale or object. Methods of specifying and training the network using a three-dimensional model of the locale or object are also disclosed.

Point tracking using a trained network

Systems and methods for frame and scene segmentation are disclosed herein. A disclosed method includes providing a frame of a scene. The scene includes a scene background. The method also includes providing a model of the scene background. The method also includes determining a frame background using the model and subtracting the frame background from the frame to obtain an approximate segmentation. The method also includes training a segmentation network using the approximate segmentation.

Scene segmentation using model subtraction

A control system may perform functions including (i) storing data indicating an association between an optical identifier and a first robot, (ii) sending, to the first robot, data encoding the optical identifier for display by the first robot, and (iii) after sending the data encoding the optical identifier, sending, to a second robot, the data indicating the association between the optical identifier and the first robot. In some examples, the first robot may receive, from the control system, data encoding a second optical identifier of the first robot so that the first robot may display the second optical identifier instead of the first optical identifier. In some examples, a first robot may capture an image of an indication of a priority status of a second robot and perform an action based on comparing a first priority status of the first robot to the second priority status of the second robot.

Communication of information regarding a robot using an optical identifier

This disclosure is directed to a hardware system for inverse graphics capture. An inverse graphics capture system (IGCS) captures data regarding a physical space that can be used to generate a photorealistic graphical model of that physical space. In certain approaches, the system includes hardware and accompanying software used to create a photorealistic six degree of freedom (6DOF) graphical model of the physical space. In certain approaches, the system includes hardware and accompanying software used for projection mapping onto the physical space. In certain approaches, the model produced by the IGCS is built using data regarding the geometry, lighting, surfaces, and environment of the physical space. In certain approaches, the model produced by the IGCS is both photorealistic and fully modifiable.

Hardware system for inverse graphics capture

Growing community of inventors

Mountain View, CA, United States of America

Ethan Rublee