Intel Corporation

Mcafee, Inc.

Other

Imtel Corporation

Rita H Wouhaybi

Tobias Max Kohlenberg

Hong C Li

Igor Tatourian

Stanley T Mo

Rajesh Poornachandran

Mark D Yarvis

Hassnaa Moustafa

Jim Santiago Baca

Giuseppe Beppe Raffa

Gyan Prakash

Francesc Guim Bernat

Shahrnaz Azizi

Alex Nayshtut

Oleg Pogorelik

Shekoufeh Qawami

Ned M Smith

Jonathan J Huang

Simon R Hunt

Jennifer Anne Healey

Barry E Huntley

Hong W Wong

Johanna M Swan

Kshitij Arun Doshi

Timothy Verrall

Marcos Emanuel Carranza

Karthik Kumar

John Charles Weast

Linda L Hurd

Tomer Rider

Omesh Tickoo

Van H Le

Glen J Anderson

Nageen Himayat

Jesse R Walker

Michael Lemay

Srikathyayani Srikanteswara

John B Vicente

Robert L Vaughn

Bradford H Needham

David W Browning

Wah Yiu Kwong

James M Okuley

Alexander Bachmutsky

Javier Felip Leon

Prosenjit Ghosh

William C Deleeuw

Rajashree Raji Baskaran

Robert Conrad Knauerhase

Stephen T Palermo

Mubashir A Mian

Steven J Birkel

Lama Nachman

Ansuya Negi

Alan D Ross

Sharad K Garg

Atul N Hatalkar

Nadine L Dabby

Lenitra M Durham

Rahul C Shah

Sangita R Sharma

Wei Yee Koay

Chieh-Yih Wan

Chaitanya Sreerama

Lady Nataly Pinilla Pico

Vinayak Honkote

Sangeeta Ghangam Manepalli

Mrigank Shekhar

David Stanasolovich

Adam Jordan

Tamir Damian Munafo

Victor Szilagyi

Cesar Martinez-Spessot

Richard T Beckwith

Lidia Warnes

Xiaoguo Liang

Scott H Robinson

Javier Sebastian Turek

Jasmeet Chhabra

Erik J Johnson

Amit S Baxi

Michael D Rosenzweig

Yi Zhang

Adi Shaliv

Joshua J Ratcliff

Alexandra C Zafiroglu

Ralf Graefe

Annie Foong

Neelam Chandwani

Mic Bowman

Thijs Metsch

Rafael Rosales

Sudip S Chahal

Mariano Tepper

Yanbing Sun

Mateo Guzman

Igor Ljubuncic

Anahit Tarkhanyan

Jose K Sia, Jr

Neslihan Kose Cihangir

Disclosed herein are systems, devices, and methods of a robot safety system for improving the safety of human-robot collaborations. The robot safety system may include a processor that receives a collaboration policy for task collaboration between a robot and a collaborator. The task collaboration may include a movement plan of the robot to move a manipulator to a collaboration position. The robot safety system may also determine a new collaboration position based on the collaboration policy. The robot safety system may also update the collaboration position of the movement plan to the new collaboration position.

Repetitive task and contextual risk analytics for human-robot collaboration

System and techniques for infrastructure managed workload distribution are described herein. An infrastructure processing unit (IPU) receives a workload that includes a workload definition. The workload definition includes stages of the workload and a performance expectation. The IPU provides the workload, for execution, to a processing unit of a compute node to which the IPU belongs. The IPU monitors execution of the workload to determine that a stage of the workload is performing outside of the performance expectation from the workload definition. In response, the IPU modifies the execution of the workload.

Infrastructure managed workload distribution

Disclosed herein are embodiments of systems and methods for diversified imitation learning for automated machines. In an embodiment, a process-profiling system obtains sensor data captured by a plurality of sensors that are arranged to observe one or more human subjects performing one or more processes to accomplish one or more tasks. The process-profiling system clusters the sensor data based on a set of one or more process-performance criteria. The process-profiling system also performs, based on the clustered sensor data, one or both of generating and updating one or more process profiles in a plurality of process profiles. The process-profiling system selects, for one or more corresponding automated machines, one or more process profiles from among the plurality of process profiles, and the process-profiling system configures the one or more corresponding automated machines to operate according to the selected one or more process profiles.

Diversified imitation learning for automated machines

Methods, apparatus, systems, and articles of manufacture are disclosed for proactive data routing. An example apparatus includes at least one memory, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to at least execute a machine-learning model to output a first data routing path in a network environment based on metadata associated with an event in the network environment. The processor circuitry is further to, after a detection of a change of the first data routing path to a second data routing path, retrain the machine-learning model to output a third data routing path based on the second data routing path. The processor circuitry is additionally to cause transmission of a second message to a first node based on the third data routing path after an identification of the event.

Systems, apparatus, articles of manufacture, and methods for proactive data routing

A robot may include a processor, configured to operate according to a first operational mode or a second operational mode, wherein the first operational mode comprises performing an environmental perception operation using first sensor data, wherein the first sensor data comprise no camera data, and wherein the second operational mode comprises performing the environmental perception operation using second sensor data, wherein the second sensor data comprise camera data; while operating according to the first operational mode, determine a confidence factor representing a confidence of the environmental perception operation; determine whether or not the confidence factor is outside of a range; and if the confidence factor is outside of the range, operate according the second operational mode.

Robot sensor data management

Methods, apparatus, systems and articles of manufacture are disclosed for edge data prioritization. An example apparatus includes at least one memory, instructions, and processor circuitry to at least one of execute or instantiate the instructions to identify an association of a data packet with a data stream based on one or more data stream parameters included in the data packet corresponding to the data stream, the data packet associated with a first priority, execute a model based on the one or more data stream parameters to generate a model output, determine a second priority of at least one of the data packet or the data stream based on the model output, the model output indicative of an adjustment of the first priority to the second priority, and cause transmission of at least one of the data packet or the data stream based on the second priority.

Systems, apparatus, and methods for edge data prioritization

Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Technologies for re-programmable hardware in autonomous vehicles

Various aspects of methods, systems, and use cases include techniques for training or using a model to control a robot. A method may include identifying a set of action primitives applicable to a set of robots, receiving information corresponding to a task (e.g., a collaborative task), and determining at least one action primitive based on the received information. The method may include training a model to control operations of at least one robot of the set of robots using the received information and the at least one action primitive.

Collaborative multi-robot tasks using action primitives

Methods, apparatus, systems and articles of manufacture to dynamically control devices based on distributed data are disclosed. An example apparatus includes a comparator to compare a first measurement measured by a first peer device to a second measurement, the second measurement being measured locally by the apparatus; and an operation adjuster to, when the comparison satisfies a threshold, adjust a measurement protocol of the first peer device.

Methods and apparatus to dynamically control devices based on distributed data

A device including a processor configured to detect an environment of an automated machine, wherein the environment comprises one or more further automated machines; determine an action taken by the one or more further automated machines; determine an action expected of the one or more further automated machines; compares the taken action with the expected action; determine an accuracy score associated with the one or more further automated machines based on the comparison.

Autonomous machine collaboration

Methods and apparatus for Time-Sensitive Networking Coordinated Transfer Learning in industrial settings are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to cause performance of an operation by a first machine according to a first configuration, process a performance metric of the performance of the operation by the first machine to determine whether the performance metric is within a threshold range, and in response to a determination that the performance metric is not within the threshold range, cause performance of the operation by a second machine according to a second configuration different from the first configuration.

Methods and apparatus for time-sensitive networking coordinated transfer learning in industrial settings

A controller for an automated machine may include including: one or more processors configured to: determine that a group affiliation of the automated machine switched from a first group of automated machines to a second group of automated machines, the first group of automated machines being assigned to one or more first tasks, the second group of automated machines being assigned to one or more second tasks; generate a message for one or more network devices of the second group of automated machines in accordance with a communication protocol, the message including information about a task performing model of the automated machine, the task performing model being based on a result of performing at least one task of the one or more first tasks by the automated machine.

Autonomous machine knowledge transfer

According to various aspects, a controller for an automated machine may include: one or more processors configured to: obtain a message from a further automated machine in accordance with a communication protocol, the message including a first result of a first sensing process that the further automated machine performs; and determine an assessment of the automated machine based on the first result and based on a second result of a second sensing process that the automated machine performs.

Automated machines and systems

Methods and systems that allow a user to see the people or groups who have access to files that are maintained by a plurality of cloud content sharing services. In particular, the user may see what specific party has access to each particular file or directory, regardless of multiple cloud content sharing services involved. Moreover, a user interface and exposed application program interface allows the user to manipulate the permissions, e.g., granting access, to another person or group, to a file or directory. The user interface may also allow the user to terminate access to the file or directory for a person or group. The user's action to change a permission may be effected independently of the particular cloud content sharing service.

Unifying interface for cloud content sharing services

Various systems and methods for implementing a software defined industrial system are described herein. For example, an orchestrated system of distributed nodes may run an application, including modules implemented on the distributed nodes. In response to a node failing, a module may be redeployed to a replacement node. In an example, self-descriptive control applications and software modules are provided in the context of orchestratable distributed systems. The self-descriptive control applications may be executed by an orchestrator or like control device and use a module manifest to generate a control system application. For example, an edge control node of the industrial system may include a system on a chip including a microcontroller (MCU) to convert IO data. The system on a chip includes a central processing unit (CPU) in an initial inactive state, which may be changed to an activated state in response an activation signal.

Distributed software-defined industrial systems

Growing community of inventors

Portland, OR, United States of America

Rita H Wouhaybi