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).
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Publication number: 20240393810Abstract: Current approaches to controlling robots from multiple vendors typically requires multiple software systems that define vendor-exclusive fleet manager or dispatch systems. Autonomous devices (e.g., robots, drones, vehicles) can be controlled from multiple vendors that use multiple locally sourced map. For example, maps from individual robots can be translated to a base map that can be used to command and control hybrid fleets of robots.Type: ApplicationFiled: October 11, 2022Publication date: November 28, 2024Applicant: Siemens CorporationInventors: Jose Luis Susa Rincon, Ines Ugalde Diaz, Michael Jaentsch, Joachim Feld
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Publication number: 20240362855Abstract: 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.Type: ApplicationFiled: August 10, 2022Publication date: October 31, 2024Applicant: Siemens AktiengesellschaftInventors: Wei Xi Xia, Eugen Solowjow, Shashank Tamaskar, Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Yash Shahapurkar, Chengtao Wen
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Publication number: 20240335941Abstract: It is recognized herein that current approaches to autonomous operations are often limited to grasping and manipulation operations that can be performed in a single step. It is further recognized herein that there are various operations in robotics (e.g., assembly tasks) that require multiple steps or a sequence of motions to be performed. To determine or plan a sequence of motions for fulfilling a task, an autonomous system that includes a robot can perform object recognition, pose estimation, affordance analysis, decision-making, probabilistic task or motion planning, and object manipulation.Type: ApplicationFiled: August 31, 2021Publication date: October 10, 2024Applicant: Siemens AktiengesellschaftInventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
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Publication number: 20240296662Abstract: A computer-implemented method for building an object detection module uses mesh representations of objects belonging to specified object classes of interest to render images by a physics-based simulator. Each rendered image captures a simulated environment containing objects belonging to multiple object classes of interest placed in a bin or on a table. The rendered images are generated by randomizing a set of parameters by the simulator to render a range of simulated environments. The randomized parameters include environmental and sensor-based parameters. A label is generated for each rendered image, which includes a two-dimensional representation indicative of location and object classes of objects in that rendered image frame. Each rendered image and the respective label constitute a data sample of a synthetic training dataset. A deep learning model is trained using the synthetic training dataset to output object classes from an input image of a real-world physical environment.Type: ApplicationFiled: August 6, 2021Publication date: September 5, 2024Applicant: Siemens CorporationInventors: Eugen Solowjow, Ines Ugalde Diaz, Yash Shahapurkar, Juan L. Aparicio Ojea, Heiko Claussen
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Publication number: 20240253234Abstract: An autonomous system can include a depth camera configured to capture a depth image of a bin that contains a plurality of objects from a first direction, so as to define a captured image. Based on the bottom end of the bin and the captured image, the system can generate a cropped region that defines a plane along a second direction and a third direction that are both substantially perpendicular to the first direction. Based on the captured image, the system can make a determination as to whether at least one object of the plurality of objects lies outside the cropped region. Based on the determination, the system can select a final region of interest for determining grasp points on the plurality of objects.Type: ApplicationFiled: December 27, 2023Publication date: August 1, 2024Applicant: Siemens AktiengesellschaftInventors: Ajay Balasubramanian, Eugen Solowjow, Ines Ugalde Diaz, Chengtao Wen
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Publication number: 20240208069Abstract: Fully flexible kitting processes can be automated by generating pick and place motions for multi-robot, multi-gripper, robotic systems.Type: ApplicationFiled: May 25, 2021Publication date: June 27, 2024Applicant: Siemens AktiengesellschaftInventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Yash Shahapurkar, Shashank Tamaskar
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Publication number: 20240198515Abstract: A covariate shift generally refers to the change of the distribution of the input data (e.g., noise distribution) between the training and inference regimes. Such covariate shifts can degrade the performance grasping neural networks, and thus robotic grasping operations. As described herein, an output of a grasp neural network can be transformed, so as to determine appropriate locations on a given object for a robot or autonomous machine to grasp.Type: ApplicationFiled: May 25, 2021Publication date: June 20, 2024Applicant: Siemens AktiengesellschaftInventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Yash Shahapurkar, Shashank Tamaskar
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Publication number: 20240198526Abstract: 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.Type: ApplicationFiled: May 25, 2021Publication date: June 20, 2024Applicant: Siemens AktiengesellschaftInventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Yash Shahapurkar, Shashank Tamaskar
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Publication number: 20240198530Abstract: In described embodiments of method for executing autonomous bin picking, a physical environment comprising a bin containing a plurality of objects is perceived by one or more sensors. Multiple artificial intelligence (AI) modules feed from the sensors to compute grasping alternatives, and in some embodiments, detected objects of interest. Grasping alternatives and their attributes are computed based on the outputs of the AI modules in a high-level sensor fusion (HLSF) module. A multi-criteria decision making (MCDM) module is used to rank the grasping alternatives and select the one that maximizes the application utility while satisfying specified constraints.Type: ApplicationFiled: June 25, 2021Publication date: June 20, 2024Applicant: Siemens CorporationInventors: Ines Ugalde Diaz, Eugen Solowjow, Juan L. Aparicio Ojea, Martin Sehr, Heiko Claussen
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Patent number: 11941451Abstract: 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: GrantFiled: August 30, 2019Date of Patent: March 26, 2024Assignee: SIEMENS AKTIENGESELLSCHAFTInventors: Ines Ugalde Diaz, Martin Sehr, Juan L. Aparicio Ojea, Michael Unkelbach
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Publication number: 20240066723Abstract: 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: ApplicationFiled: August 7, 2023Publication date: February 29, 2024Inventors: Eduardo Moura Cirilo Rocha, Husnu Melih Erdogan, Eugen Solowjow, Ines Ugalde Diaz, Yash Shahapurkar, Nan Tian, Paul Andreas Batsii, Christopher Schuette
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Publication number: 20240012400Abstract: 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: ApplicationFiled: August 28, 2020Publication date: January 11, 2024Applicant: Siemens CorporationInventors: Shashank Tamaskar, Martin Sehr, Eugen Solowjow, Wei Xi Xia, Juan L. Aparicio Ojea, Ines Ugalde Diaz
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Publication number: 20230359864Abstract: 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: ApplicationFiled: August 31, 2020Publication date: November 9, 2023Applicant: Siemens CorporationInventors: Martin Sehr, Eugen Solowjow, Wei Xi Xia, Shashank Tamaskar, Ines Ugalde Diaz, Heiko Claussen, Juan L. Aparicio Ojea
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Publication number: 20230316115Abstract: 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: ApplicationFiled: August 28, 2020Publication date: October 5, 2023Applicant: Siemens AktiengesellschaftInventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
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Publication number: 20230305574Abstract: 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: ApplicationFiled: March 10, 2023Publication date: September 28, 2023Applicant: Siemens AktiengesellschaftInventors: Ines Ugalde Diaz, Eugen Solowjow, Yash Shahapurkar, Husnu Melih Erdogan, Eduardo Moura Cirilo Rocha
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Publication number: 20230214665Abstract: 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: ApplicationFiled: April 17, 2020Publication date: July 6, 2023Inventors: Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar, Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen
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Publication number: 20230108920Abstract: 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: ApplicationFiled: March 30, 2020Publication date: April 6, 2023Inventors: Ines Ugalde Diaz, Heiko Claussen, Juan L. Aparicio Ojea, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
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Publication number: 20230050387Abstract: 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: ApplicationFiled: February 11, 2020Publication date: February 16, 2023Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
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Publication number: 20220410391Abstract: 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: ApplicationFiled: November 22, 2019Publication date: December 29, 2022Inventors: Juan L. Aparicio Ojea, Heiko Claussen, Ines Ugalde Diaz, Martin Sehr, Eugen Solowjow, Chengtao Wen, Wei Xi Xia, Xiaowen Yu, Shashank Tamaskar
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Publication number: 20220391565Abstract: 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: ApplicationFiled: May 25, 2022Publication date: December 8, 2022Inventors: Chengtao Wen, Juan L. Aparicio Ojea, Ines Ugalde Diaz, Gokul Narayanan Sathya Narayanan, Eugen Solowjow, Wei Xi Xia, Yash Shahapurkar, Shashank Tamaskar, Heiko Claussen