Patents by Inventor Ines Ugalde Diaz

Ines Ugalde Diaz has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240066723
    Abstract: It is recognized It is recognized herein that current approaches to robotic picking lack efficiency and capabilities. In particular, current approaches often do not properly or efficiently estimate the pose of bins, due to various technical challenges in doing so, which can impact grasp computations and overall performance of a given robot. The pose of the bin can be determined or estimated based on depth images. Such bin pose estimation can be performed during runtime of a given robot, such that grasping can be enhanced due to the bin pose estimations.
    Type: Application
    Filed: August 7, 2023
    Publication date: February 29, 2024
    Inventors: Eduardo Moura Cirilo Rocha, Husnu Melih Erdogan, Eugen Solowjow, Ines Ugalde Diaz, Yash Shahapurkar, Nan Tian, Paul Andreas Batsii, Christopher Schuette
  • Publication number: 20240012400
    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.
    Type: Application
    Filed: August 28, 2020
    Publication date: January 11, 2024
    Applicant: Siemens Corporation
    Inventors: Shashank Tamaskar, Martin Sehr, Eugen Solowjow, Wei Xi Xia, Juan L. Aparicio Ojea, Ines Ugalde Diaz
  • Publication number: 20230359864
    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.
    Type: Application
    Filed: August 31, 2020
    Publication date: November 9, 2023
    Applicant: Siemens Corporation
    Inventors: Martin Sehr, Eugen Solowjow, Wei Xi Xia, Shashank Tamaskar, Ines Ugalde Diaz, Heiko Claussen, Juan L. Aparicio Ojea
  • Publication number: 20230316115
    Abstract: A computer-implemented method includes operating a controllable physical device to perform a task. The method also includes miming forward simulations of the task by a physics engine based on one or more physics parameters. The physics engine communicates with a parameter data layer where each of the one or more physics parameters is modeled with a probability distribution. For each forward simulation run, a tuple of parameter values is sampled from the probability distribution of the one or more physics parameters and fed to the physics engine. The method includes obtaining an observation pertaining to the task from the physical environment and a corresponding forward simulation outcome associated with each sampled tuple of parameter values. The method then includes updating the probability distribution of the one or more physics parameters in the parameter data layer based on the observation from the physical environment and the corresponding forward simulation outcomes.
    Type: Application
    Filed: August 28, 2020
    Publication date: October 5, 2023
    Applicant: Siemens Aktiengesellschaft
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20230305574
    Abstract: It is recognized herein that robots or autonomous systems can lose time when computing grasp scores for empty bins. Further, when grasps are attempted on empty bins, for instance due to the related grasp score computations, the robot can lose additional time through being used unnecessarily to attempt the grasp. Such usage can wear on the robot, or damage the robot, in some cases. An autonomous system can classify or determine whether a bin contains an object or is empty, for example, such that a grasp computation is not performed when the bin is empty. In some examples, a system classifies a given bin at runtime before each grasp computation is performed. Thus, systems described herein can avoid performing unnecessary grasp computations, thereby conserving processing time and overheard, among addressing other technical problems.
    Type: Application
    Filed: March 10, 2023
    Publication date: September 28, 2023
    Applicant: Siemens Aktiengesellschaft
    Inventors: Ines Ugalde Diaz, Eugen Solowjow, Yash Shahapurkar, Husnu Melih Erdogan, Eduardo Moura Cirilo Rocha
  • Publication number: 20230214665
    Abstract: Distributed neural network boosting is performed by a neural network system through operating at least one processor. A method comprises providing a boosting algorithm that distributes a model among a plurality of processing units each being a weak learner of multiple weak learners that can perform computations independent from one another yet process data concurrently. The method further comprises enabling a distributed ensemble learning which enables a programmable logic controller (PLC) to use more than one processing units of the plurality of processing units to scale an application and training the multiple weak learners using the boosting algorithm. The multiple weak learners are machine learning models that do not capture an entire data distribution and are purposefully designed to predict with a lower accuracy. The method further comprises using the multiple weak learners to vote for a final hypothesis based on a feed forward computation of neural networks.
    Type: Application
    Filed: April 17, 2020
    Publication date: July 6, 2023
    Inventors: Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar, Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen
  • Publication number: 20230108920
    Abstract: A system for supporting artificial intelligence inference in an edge computing device associated with a physical process or plant includes a neural network training module, a neural network testing module and a digital twin of the physical process or plant. The neural network training module is configured to train a neural network model for deployment to the edge computing device based on data including baseline training data and field data received from the edge computing device. The neural network testing module configured to validate the trained neural network model prior to deployment to the edge computing device by leveraging the digital twin of the physical process or plant.
    Type: Application
    Filed: March 30, 2020
    Publication date: April 6, 2023
    Inventors: Ines Ugalde Diaz, Heiko Claussen, Juan L. Aparicio Ojea, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20230050387
    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.
    Type: Application
    Filed: February 11, 2020
    Publication date: February 16, 2023
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20220410391
    Abstract: In current applications of autonomous machines in industrial settings, the environment, in particular the devices and systems with which the machine interacts, is known such that the autonomous machine can operate in the particular environment successfully. Thus, current approaches to automating tasks within varying environments, for instance complex environments having uncertainties, lack capabilities and efficiencies. In an example aspect, a method for operating an autonomous machine within a physical environment includes detecting an object within the physical environment. The autonomous machine can determine and perform a principle of operation associated with a detected subcomponent of the object, so as to complete a task that requires that the autonomous machine interacts with the object. In some cases, the autonomous machine has not previously encountered the object.
    Type: Application
    Filed: November 22, 2019
    Publication date: December 29, 2022
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
  • Publication number: 20220391565
    Abstract: A method for automatically generating a bill of process in a manufacturing system comprising: receiving design information representative of a product to be produced; iteratively performing simulations of the manufacturing system; identifying manufacturing actions based on the simulations; optimizing the identified manufacturing actions to efficiently produce the product to be produced; generating, by the manufacturing system, a bill of process for producing the product. Simulations may be performed using a digital twin of the product being produced and a digital twin of the environment. System actions are optimized using a reinforcement learning technique to automatically produce a bill of process based on the design information of the product and task specifications.
    Type: Application
    Filed: May 25, 2022
    Publication date: December 8, 2022
    Inventors: Chengtao Wen, Juan L. Aparicio Ojea, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Eugen Solowjow, Wei Xi Xia, Yash Shahapurkar, Shashank Tamaskar, Heiko Claussen
  • Publication number: 20220297295
    Abstract: A computer-implemented method for designing execution of a process by a robotic cell includes obtaining a process goal and one or more process constraints. The method includes accessing a library of constructs and a library of skills. Each construct includes a digital representation of a component of the robotic cell or a geometric transformation of the robotic cell. Each skill includes a functional description for using a robot of the robotic cell to interact with a physical environment to perform a skill objective. The method uses a simulation engine to simulate a multiplicity of designs, wherein each design is characterized by a combination of constructs and skills to achieve the process goal, and determine a set of feasible designs that meet the one or more process constraints. The method includes outputting recommended designs from the set of feasible designs.
    Type: Application
    Filed: February 8, 2022
    Publication date: September 22, 2022
    Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Yash Shahapurkar, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Gokul Narayanan Sathya Narayanan, Shashank Tamaskar
  • Publication number: 20220035670
    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.
    Type: Application
    Filed: August 30, 2019
    Publication date: February 3, 2022
    Inventors: Ines Ugalde Diaz, Martin Sehr, Juan L. Aparicio Ojea, Michael Unkelbach