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公开(公告)号：US20240362855A1
公开(公告)日：2024-10-31
申请号：US18291700
申请日：2022-08-10
发明人： Wei Xi Xia , Eugen Solowjow , Shashank Tamaskar , Juan L. Aparicio Ojea , Heiko Claussen , Ines Ugalde Diaz , Gokul Narayanan Sathya Narayanan , Yash Shahapurkar , Chengtao Wen
CPC分类号： G06T15/205 , G06F30/10 , G06T7/55 , G06T17/00 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
摘要： 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.
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公开(公告)号：US20240253234A1
公开(公告)日：2024-08-01
申请号：US18397197
申请日：2023-12-27
IPC分类号： B25J9/16
CPC分类号： B25J9/1697 , B25J9/1612
摘要： 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.
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公开(公告)号：US11979419B2
公开(公告)日：2024-05-07
申请号：US17442901
申请日：2019-04-09
发明人： Chengtao Wen , Mohamed El Amine Houyou , Juan L. Aparicio Ojea , Mathias Maurmaier , Martin Sehr , Tao Cui
CPC分类号： H04L63/1425 , G05B23/0272 , G05B2223/06
摘要： Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.
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公开(公告)号：US11883947B2
公开(公告)日：2024-01-30
申请号：US17761613
申请日：2019-09-30
CPC分类号： B25J9/1697 , B25J13/08 , G06F30/10
摘要： A system controller for visual servoing includes a technology module with dedicated hardware acceleration for deep neural network that retrieves a desired configuration of a workpiece object being manipulated by a robotic device and receives visual feedback information from one or more sensors on or near the robotic device that includes a current configuration of the workpiece object. The hardware accelerator executes a machine learning model trained to process the visual feedback information and determine a configuration error based on a difference between the current configuration of the workpiece object and the desired configuration of the workpiece object. A servo control module adapts a servo control signal to the robotic device for manipulation of the workpiece object in response to the configuration error.
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公开(公告)号：US20220201026A1
公开(公告)日：2022-06-23
申请号：US17442901
申请日：2019-04-09
发明人： Chengtao Wen , Mohamed El Amine Houyou , Juan L. Aparicio Ojea , Mathias Maurmaier , Martin Sehr , Tao Cui
摘要： Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.
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公开(公告)号：US20210107142A1
公开(公告)日：2021-04-15
申请号：US16970450
申请日：2018-09-13
IPC分类号： B25J9/16
摘要： Systems and methods for controlling robots including industrial robots. A method includes executing (402) a program (550) to control a robot (102) by the robot control system (120, 500). The method includes receiving (404) robot state information (554). The method includes receiving (406) force torque feedback (556) inputs from a sensor (554) on the robot (102). The method includes producing (410) a robot control command for the robot (102) based on the robot state information (554) and the force torque feedback (556) inputs. The method includes controlling (412) the robot (102) using the robot control command.
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27.发明申请公开(公告)号：US20200262064A1
公开(公告)日：2020-08-20
申请号：US16788840
申请日：2020-02-12
IPC分类号： B25J9/16
摘要： Computerized system and method are provided. A robotic manipulator (12) is arranged to grasp objects (20). A gripper (16) is attached to robotic manipulator (12), which includes an imaging sensor (14). During motion of robotic manipulator (12), imaging sensor (14) is arranged to capture images providing different views of objects in the environment of the robotic manipulator. A processor (18) is configured to find, based on the different views, candidate grasp locations and trajectories to perform a grasp of a respective object in the environment of the robotic manipulator. Processor (18) is configured to calculate respective values indicative of grasp quality for the candidate grasp locations, and, based on the calculated respective values indicative of grasp quality for the candidate grasp locations, processor (18) is configured to select a grasp location likely to result in a successful grasp of the respective object.
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