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.

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.

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.

Abstract: One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors structured to inject fuel into the dedicated EGR cylinder and the non-dedicated EGR cylinder, and an electronic control system operatively coupled with the fueling system and the ignition system. The electronic control system is configured to evaluate engine operating parameters including an engine load and an engine speed. The electronic control system is responsive to variation of the engine operating parameters to control operation of the fueling system to vary combustion in the at least one dedicated cylinder between rich of stoichiometric and stoichiometric.

Abstract: Robots might interact with planar objects (e.g., garments) for process automation, quality control, to perform sewing operations, or the like. It is recognized herein that robots interacting with such planar objects can pose particular problems, for instance problems related to detecting the planar object and estimating the pose of the detected planar object. A system can be configured to detect or segment planar objects, such as garments. The system can include a three-dimensional (3D) sensor positioned to detect a planar object along a transverse direction. The system can further include a first surface that supports the planar object. The first surface can be positioned such that the planar object is disposed between the first surface and the 3D sensor along the transverse direction. In various examples, the 3D sensor is configured to detect the planar object without detecting the first surface.

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.

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.

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.

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.

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.

Abstract: One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors, an ignition system including a plurality of spark plugs, an intake throttle, and an electronic control system. The electronic control system is configured to control combustion during transient operation of the engine by determining one or more combustion control parameters compensating for variation of one or more of inert matter, unburned air and unburned fuel in EGR output by the dedicated EGR cylinder during transient operation of the engine, and an effect of the EGR loop on inert matter, unburned air and unburned fuel provided to the plurality of cylinders, and controlling operation of at least one of the throttle, the ignition system and the plurality of injectors in response to at least one of the one or more combustion control parameters.

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.

Abstract: One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors structured to inject fuel into the dedicated EGR cylinder and the non-dedicated EGR cylinder, and an electronic control system operatively coupled with the fueling system and the ignition system. The electronic control system is configured to evaluate engine operating parameters including an engine load and an engine speed. The electronic control system is responsive to variation of the engine operating parameters to control operation of the fueling system to vary combustion in the at least one dedicated cylinder between rich of stoichiometric and stoichiometric.

Abstract: One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors structured to inject fuel into the dedicated EGR cylinder and the non-dedicated EGR cylinder, and an electronic control system operatively coupled with the fueling system and the ignition system. The electronic control system is configured to evaluate engine operating parameters including an engine load and an engine speed. The electronic control system is responsive to variation of the engine operating parameters to control operation of the fueling system to vary combustion in the at least one dedicated cylinder between rich of stoichiometric and stoichiometric.

Abstract: An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value.

Abstract: One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors structured to inject fuel into the dedicated EGR cylinder and the non-dedicated EGR cylinder, and an electronic control system operatively coupled with the fueling system and the ignition system. The electronic control system is configured to evaluate engine operating parameters including an engine load and an engine speed. The electronic control system is responsive to variation of the engine operating parameters to control operation of the fueling system to vary combustion in the at least one dedicated cylinder between rich of stoichiometric and stoichiometric.

Abstract: An engine system includes an air handling and fuel system whose states are managed by a reference managing unit. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The reference managing unit includes a controller which controls the air handling and fuel system of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines air handling references and fuel system references by using the policy function after receiving the current state as an input, and outputs the air handling references and fuel system references to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value.

Abstract: One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors, an ignition system including a plurality of spark plugs, an intake throttle, and an electronic control system. The electronic control system is configured to control combustion during transient operation of the engine by determining one or more combustion control parameters compensating for variation of one or more of inert matter, unburned air and unburned fuel in EGR output by the dedicated EGR cylinder during transient operation of the engine, and an effect of the EGR loop on inert matter, unburned air and unburned fuel provided to the plurality of cylinders, and controlling operation of at least one of the throttle, the ignition system and the plurality of injectors in response to at least one of the one or more combustion control parameters.

Abstract: An engine system includes an air handling and fuel system whose states are managed by a reference managing unit. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The reference managing unit includes a controller which controls the air handling and fuel system of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines air handling references and fuel system references by using the policy function after receiving the current state as an input, and outputs the air handling references and fuel system references to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value.

Abstract: An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value.