Teradyne, Inc.

Eric Lenhart Truebenbach

Evgeny Polyakov

Brian S Merrow

Edward Garcia

Brian J Whitaker

John P Toscano

Marc LeSueur Smith

Scott Noble

Peter Lustig

Chu-Yin Chang

David Walter Lewinnek

Lloyd K Frick

Michael Francis McGoldrick

Douglas E Barker

Jiann-Neng Chen

Walter Vahey

David Demirdjian

Richard P Davis

John Joseph Arena

David John Lind

Christopher Thomas Aloisio

Stephen R Fairbanks

Tung L Ng

Teresa P Lopes

Chris Behling

Michael Mcgoldrick

Evgeny c/o Teradyne Inc Polyakov

A method and computing system comprising identifying one or more candidate objects for selection by a robot. A path to the one or more candidate objects may be determined based upon, at least in part, a robotic environment and at least one robotic constraint. A feasibility of grasping a first candidate object of the one or more candidate objects may be validated. If the feasibility is validated, the robot may be controlled to physically select the first candidate object. If the feasibility is not validated, at least one of a different grasping point of the first candidate object, a second path, or a second candidate object may be selected.

System and method for robotic bin picking

Embodiments of the present disclosure are directed towards a robotic system. The system may include a robot configured to receive an initial constrained approach for performing a robot task. The system may further include a graphical user interface in communication with the robot. The graphical user interface may be configured to allow a user to interact with the robot to determine an allowable range of robot poses associated with the robot task. The allowable range of robot poses may include fewer constraints than the initial constrained approach. The allowable range of poses may be based upon, at least in part, one or more degrees of symmetry associated with a workpiece associated with the robot task or an end effector associated with the robot. The system may also include a processor configured to communicate the allowable range of robot poses to the robot.

System and method for constraint management of one or more robots

Display screen or portion thereof with graphical user interface

A computing system is provided for training one or more machine learning models to perform at least a portion of a robotic task of a physical robotic system by monitoring a model-based control algorithm associated with the physical robotic system perform at least a portion of the robotic task. One or more robotic task predictions may be defined, via the one or more machine learning models, based upon, at least in part, the training of the one or more machine learning models. The one or more robotic task predictions may be provided to the model-based control algorithm associated with the physical robotic system. The robotic task may be performed, via the model-based control algorithm associated with the robotic system, on the physical robotic system based upon, at least in part, the one or more robotic task predictions defined by the one or more machine learning models.

System and method for predicting robotic tasks with deep learning

An example gripper may include: a base; two or more fingers attached to the base, with each finger being movable towards, and away from, one or more others of the fingers; and one or more ports at the base or at one or more of the fingers to provide suction through a vacuum.

Grasping gripper

An example system for testing camera modules includes: a structure for holding camera modules under test, where each of the camera modules includes an image sensor, and where the camera modules are arranged in a same plane on the structure and offset from each other in the plane; and a target for use in testing the camera modules concurrently, where the target includes multiple tiles that are repeated, where each tile contains information for use in testing a camera module, and where the camera modules and the tiles are arranged so that different camera modules face different tiles but see at least some of the same information at the same angle.

Matrix testing targets

A storage device transfer station is provided for transferring storage devices from a human operator to automated machinery for testing. The storage device transfer station includes a plurality of slots each capable of holding a storage device. The plurality of slots is arranged in at least one field, and the field is arranged between two parallel planes. Each slot has a first open end and a second open end, such that each open end is accessible for loading and unloading a storage device. The first open ends are accessible at a first plane of the two parallel planes and the second open ends are accessible at a second plane of the two parallel planes.

Bulk transfer of storage devices using manual loading

A memory address filter is configurable to emulate memory overrun performance of a legacy memory using an electronic memory of equal or greater capacity. The address filter includes a comparator configured to determine whether a target address is greater than a maximum legacy-address. Memory emulation at target address values greater than the maximum legacy-address value includes one or more of inhibiting the memory transaction; accomplishing the requested memory transaction at the maximum legacy-address value; and accomplishing the requested memory transaction at an address equivalent to the target address wrapped according to the maximum legacy-address value. In some embodiments, the address filter accepts one or more configuration parameters, such as memory depth, wrap-around, and overwrite enable.

Apparatus and method for controlling memory overrun

A method of transferring storage devices within a storage device testing system includes actuating an automated transporter to retrieve multiple storage devices presented for testing, and actuating the automated transporter to deliver each retrieved storage device to a respective test slot of the storage device testing system and insert each storage device in the respective test slot.

Transferring storage devices within storage device testing systems

A method of transferring storage devices within a storage device testing system includes actuating an automated transporter to substantially simultaneously retrieve multiple storage devices presented for testing, and actuating the automated transporter to substantially simultaneously deliver each retrieved storage device to a respective test slot of the storage device testing system and substantially simultaneously insert each storage device in the respective test slot.

A method of supplying disk drives to a disk drive testing system includes placing a disk drive tote, carrying multiple disk drives, in a presentation position accessible to an automated transporter of the disk drive testing system. The method includes actuating the automated transporter to retrieve one of the disk drives from the disk drive tote, and actuating the automated transporter to deliver the retrieved disk drive to a test slot of the disk drive testing system and insert the disk drive in the test slot.

Bulk feeding disk drives to disk drive testing systems

A method of controlling a temperature of a subject test slot in a cluster of test slots includes evaluating a request for a temperature change for the subject test slot to determine if sufficient power is available to achieve the requested temperature change, and inhibiting the requested temperature change unless or until sufficient power is determined to be available to achieve the requested temperature change.

Dependent temperature control within disk drive testing systems

A method of transferring disk drives within a disk drive testing system includes actuating an automated transporter to retrieve multiple disk drives presented for testing, and actuating the automated transporter to deliver each retrieved disk drive to a respective test slot of the disk drive testing system and insert each disk drive in the respective test slot.

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Sudbury, MA, United States of America

Eric Lenhart Truebenbach