公开(公告)号：WO2023018752A1

公开(公告)日：2023-02-16

申请号：PCT/US2022/039889

申请日：2022-08-10

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： XIA, Wei Xi ,  SOLOWJOW, Eugen ,  TAMASKAR, Shashank ,  APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SHAHAPURKAR, Yash ,  SATHYA NARAYANAN, Gokul Narayanan ,  WEN, Chengtao

IPC: G06F30/10 ,  G06T7/50 ,  G06N3/04

Abstract: System and method are disclosed for training a generative adversarial network pipeline that can produce realistic artificial depth images useful as training data for deep learning networks used for robotic tasks. A generator network receives a random noise vector and a computer aided design (CAD) generated depth image and generates an artificial depth image. A discriminator network receives either the artificial depth image or a real depth image in alternation, and outputs a predicted label indicating a discriminator decision as to whether the input is the real depth image or the artificial depth image. Training of the generator network is performed in tandem with the discriminator network as a generative adversarial network. A generator network cost function minimizes correctly predicted labels, and a discriminator cost function maximizes correctly predicted labels.

公开(公告)号：WO2020167641A1

公开(公告)日：2020-08-20

申请号：PCT/US2020/017460

申请日：2020-02-10

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： CLAUSSEN, Heiko ,  APARICIO OJEA, Juan L. ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  WEN, Chengtao

IPC: G06N3/04

Abstract: A system includes a robot device that comprises a non-transitory computer readable medium and a robot controller. The non-transitory computer readable medium stores one or more machine-specific modules comprising base neural network layers. The robot controller receives a task-specific module comprising information corresponding to one or more task specific neural network layers enabling performance of a task. The robot controller collects one or more values from an operating environment, and uses the values as input to a neural network comprising the base neural network layers and the task-specific neural network layers to generate an output value. The robot controller may then perform the task based on the output value.

公开(公告)号：WO2021162681A1

公开(公告)日：2021-08-19

申请号：PCT/US2020/017702

申请日：2020-02-11

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  WEN, Chengtao ,  XIA, Wei Xi ,  YU, Xiaowen ,  TAMASKAR, Shashank

IPC: G06F8/30 ,  G06F8/34 ,  G05B19/02 ,  B25J13/00 ,  B25J9/00

Abstract: According to an aspect of the present disclosure, a computer-implemented includes creating a plurality of basic skill functions for a controllable physical device of an autonomous system. Each basic skill function includes a functional description for using the controllable physical device to interact with a physical environment to perform a defined objective. The method further includes selecting one or more basic skill functions to configure the controllable physical device to perform a defined task. The method also includes determining a decorator skill function specifying at least one constraint. The decorator skill function is configured to impose, at run-time, the at least one constraint, on the one or more basic skill functions. The method further includes generating executable code by applying the decorator skill function to the one or more basic skill functions, and actuating the controllable physical device using the executable code.

公开(公告)号：WO2020072155A1

公开(公告)日：2020-04-09

申请号：PCT/US2019/049033

申请日：2019-08-30

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： UGALDE DIAZ, Ines ,  SEHR, Martin ,  APARICIO OJEA, Juan L. ,  UNKELBACH, Michael

IPC: G06F9/50

Abstract: A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine. The schedule and allocation scheme define when each of the plurality of computing tasks is performed, and which of the plurality of selected computing instances performs each of the plurality of computing tasks. The selected computing instances execute the plurality of computing tasks according to the schedule and allocation scheme.

公开(公告)号：WO2020040763A1

公开(公告)日：2020-02-27

申请号：PCT/US2018/047628

申请日：2018-08-23

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： WEN, Chengtao ,  XIA, Wei Xi ,  APARICIO OJEA, Juan L.

IPC: G06N3/00 ,  G06N3/04 ,  G06N3/08 ,  G06N7/00

Abstract: Systems and methods provide real-time production scheduling by integrating deep reinforcement learning and Monte Carlo tree search. A manufacturing process simulator is used to train a deep reinforcement learning agent to identify the sub-optimal policies for a production schedule. A Monte Carlo tree search agent is implemented to speed up the search for near-optimal policies of higher quality from the sub-optimal policies.

公开(公告)号：WO2017007479A1

公开(公告)日：2017-01-12

申请号：PCT/US2015/039670

申请日：2015-07-09

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： WANG, Lingyun ,  LAMPARTER, Steffen ,  APARICIO OJEA, Juan L.

IPC: G05B23/02 ,  G05B19/408

CPC classification number: G05B23/0264 ,  G05B19/058 ,  G05B23/0243 ,  G05B23/027 ,  G06F11/362 ,  G06F2201/86

Abstract: A method of generating events based on automation system data in an intelligent programmable logic controller includes the intelligent programmable logic controller collecting automation system data and applying a context model to the automation system data to create contextualized data. The intelligent programmable logic controller generates one or more events based on the contextualized data.

Abstract translation: 基于智能可编程逻辑控制器中的自动化系统数据生成事件的方法包括智能可编程逻辑控制器收集自动化系统数据并将上下文模型应用于自动化系统数据以创建上下文数据。 智能可编程逻辑控制器基于语境化数据生成一个或多个事件。

公开(公告)号：WO2022046104A1

公开(公告)日：2022-03-03

申请号：PCT/US2020/048735

申请日：2020-08-31

Applicant: SIEMENS CORPORATION ,  SIEMENS INDUSTRY, INC.

Inventor： SEHR, Martin ,  SOLOWJOW, Eugen ,  XIA, Wei Xi ,  TAMASKAR, Shashank ,  UGALDE DIAZ, Ines ,  CLAUSSEN, Heiko ,  APARICIO OJEA, Juan L.

IPC: G06N3/063 ,  G06N3/08

Abstract: An edge device can be configured to perform industrial control operations within a production environment that defines a physical location. The edge device can include a plurality of neural network layers that define a deep neural network. The edge device be configured to obtain data from one or more sensors at the physical location defined by the production environment. The edge device can be further configured to perform one or more matrix operations on the data using the plurality of neural network layers so as to generate a large scale matrix computation at the physical location defined by the production environment. In some examples, the edge device can send the large scale matrix computation to a digital twin simulation model associated with the production environment, so as to update the digital twin simulation model in real time.

公开(公告)号：WO2022046062A1

公开(公告)日：2022-03-03

申请号：PCT/US2020/048315

申请日：2020-08-28

Applicant: SIEMENS CORPORATION

Inventor： TAMASKAR, Shashank ,  SEHR, Martin ,  SOLOWJOW, Eugen ,  XIA, Wei Xi ,  APARICIO OJEA, Juan L. ,  UGALDE DIAZ, Ines

IPC: G05B19/418

Abstract: A computer-implemented method for failure classification of a surface treatment process includes receiving one or more process parameters that influence one or more failure modes of the surface treatment process and receiving sensor data pertaining to measurement of one or more process states pertaining to the surface treatment process. The method includes processing the received one or more process parameters and the sensor data by a machine learning model deployed on an edge computing device controlling the surface treatment process to generate an output indicating, in real-time, a probability of process failure via the one or more failure modes. The machine learning model is trained on a supervised learning regime based on process data and failure classification labels obtained from physics simulations of the surface treatment process in combination with historical data pertaining to the surface treatment process.

公开(公告)号：WO2019125519A1

公开(公告)日：2019-06-27

申请号：PCT/US2018/044277

申请日：2018-07-30

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： CLAUSSEN, Heiko ,  STERZING, Volkmar ,  APARICIO OJEA, Juan L.

IPC: G05B19/05

CPC classification number: G05B19/056

Abstract: Over the past several decades, rapid advances in semiconductors, automation, and control systems have resulted in the adoption of programmable logic controllers (PLCs) in an immense variety of environments. Machine learning techniques help train replacement PLCs when a legacy PLC must be replaced, e.g., due to aging or failure. The techniques facilitate the efficient adoption and correct operation of replacement PLCs in the industrial environment.

公开(公告)号：WO2022250659A1

公开(公告)日：2022-12-01

申请号：PCT/US2021/034035

申请日：2021-05-25

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： APARICIO OJEA, Juan L. ,  CLAUSSEN, Heiko ,  UGALDE DIAZ, Ines ,  SATHYA NARAYANAN, Gokul Narayanan ,  SOLOWJOW, Eugen ,  WEN, Chengtao ,  XIA, Wei Xi ,  SHAHAPURKAR, Yash ,  TAMASKAR, Shashank

IPC: B25J9/16

Abstract: In some cases, grasp point algorithms can be implemented so as to compute grasp points on an object that enable a stable grasp. It is recognized herein, however, that in practice a robot in motion can drop the object or otherwise have grasp issues when the object is grasped at the computed stable grasp points. Path constraints that can differ based on a given object are generated while generating the trajectory for a robot, so as to ensure that a grasp remains stable throughout the motion of the robot.