Abb Schweiz Ag

Dodge Industrial, Inc.

Dodge Acquisition Co.

Jinmiao Huang

Carlos Martinez

Sangeun Choi

Stefan Rakuff

Thomas A Fuhlbrigge

Biao Zhang

Jianjun Wang

Remus Boca

Carlos Winsor Morato

Johnny Holmberg

Zhou Teng

A system for data driven diagnostics of a machine including a machine learning model instantiated in a computer, the machine learning model being configured to: receive operational data of the machine; and process the operational data to determine machine diagnostics information. The machine learning model is trained using simulated defect information received from a simulation environment.

Method and system for data driven machine diagnostics

For training an object picking robot with real and simulated grasp performance data, grasp locations on an object are assigned based on object physical properties. A simulation experiment for robot grasping is performed using a first set of assigned locations. Based on simulation data from the simulation, a simulated object grasp quality of the robot is evaluated for each of the assigned locations. A first set of candidate grasp locations on the object is determined based on data representative of simulated grasp quality from the evaluation. Based on sensor data from an actual experiment for the robot grasping using each of the candidate grasp locations, an actual object grasp quality is evaluated for each of the candidate locations.

Hybrid machine learning-based systems and methods for training an object picking robot with real and simulated performance data

A robot is configured to perform a task on an object using a method for generating a 3D model sufficient to determine a collision free path and identify the object in an industrial scene. The method includes determining a predefined collision free path and scanning an industrial scene around the robot. Stored images of the industrial scene are retrieved from a memory and analyzed to construct a new 3D model. After an object is detected in the new 3D model, the robot can further scan the image in the industrial scene while moving along a collision free path until the object is identified at a predefined certainty level. The robot can then perform a robot task on the object.

Method and system to generate a 3D model for a robot scene

A grasping system includes a robotic arm having a gripper. A fixed sensor monitors a grasp area and an onboard sensor moves with the gripper also monitors the area. A controller receives information indicative of a position of an object to be grasped and operates the robotic arm to bring the gripper into a grasp position adjacent the object based on information provided by the fixed sensor. The controller is also programmed to operate the gripper to grasp the object in response to information provided by the first onboard sensor.

Object grasp system and method

A method for automated gearbox design includes: instantiating the gearbox model having an initial parameter state in a modeling environment; analyzing and/or characterizing the gearbox model in the modeling environment to determine gearbox model performance; and determining whether the gearbox model performance satisfies a performance target. Upon a determination that the gearbox model performance does not satisfy the performance target: a reward is calculated based on the gearbox model performance; a reinforcement machine learning agent determines a parameter change action based on the reward and a current parameter state of the gearbox model; and an updated parameter state of the gearbox model is determined based on the parameter change action.

Method and system for automated gear reducer design

Methods, systems, and software for controlling object picking and placement by a robot system are disclosed. The method includes assigning machine learning training data of a machine learning model for an object. The machine learning training data includes a plurality of known grasp location labels assigned to the object positioned in a plurality of different object poses. The method includes providing the object in a work space of the robot system. For the object in the work space in a first pose of the plurality of different object poses, the method includes: mapping a first candidate grasp location on the object; executing robotic movements for the first candidate grasp location on the object; and evaluating a result of the executing for the first candidate grasp location according to at least one predetermined performance criteria.

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Chongqing, China

Jinmiao Huang