Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

An access management system includes a mobile device with a processor and a memory and a software platform including at least a processor and a memory. The software platform is configured to generate a polymorphic access key, and the mobile device is configured to receive the polymorphic access key and display the polymorphic access key or communicate the polymorphic access key to an access management device. The access management device is configured to provide access to an account, another device, a physical space, or a virtual space upon reading or receiving the polymorphic access key and verifying the polymorphic access key.

An access management system includes a mobile device with a processor and a memory and a software platform including at least a processor and a memory. The software platform is configured to generate a polymorphic access key, and the mobile device is configured to receive the polymorphic access key and display the polymorphic access key or communicate the polymorphic access key to an access management device. The access management device is configured to provide access to an account, another device, a physical space, or a virtual space upon reading or receiving the polymorphic access key and verifying the polymorphic access key.

A method of manufacturing a composite structure includes accessing design data for the composite structure that is manufactured according to a process including forming a layup of plies of fibers using a machine tool. The method includes applying the design data to an ANN classifier to classify a localized inconsistency of a type of inconsistency on the composite structure, the localized inconsistency spatially referenced to a location on the composite structure. The method includes performing a root cause analysis to identify one or more of process parameters as a potential cause of the type of inconsistency, and modifying one or more of the geometric model, the layup design, or values of the one or more of the process parameters to address the potential cause.

Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

A crimp machine is provided and includes an anvil having a lower die having a lower forming surface. The anvil is configured to support a crimp barrel of a terminal that receives a wire. The crimp machine includes a press having an upper die having an upper forming surface. The press is movable relative to the anvil during a crimping process to connect the crimp barrel to the wire. A crimp zone is defined between the upper forming surface and the lower forming surface. The crimp machine includes a vision system positioned to view the crimp zone. The vision system includes an imaging device configured to image the crimp barrel of the terminal and the wire. The vision system operates the imaging device to capture an image prior to the crimping process for performing a validation pre-check prior to the crimping process.

An automation system (1) determines control data (S′), outputs same to controlled elements (5) of the facility (ANL) and thereby controls the facility (ANL). Sensor devices (2) acquire measurement data (M) of the facility (ANL) and at least partly feed same to the automation system (1) and a man-machine interface (3). Said man-machine interface (3) receives planning data (P) from a production planning system (11) and/or control data (S′) and/or internal data (I) from the automation system (1). The interface outputs the data (M, S′, I) to a person (4). It furthermore receives control commands (S) from the person (4) and forwards them to the automation system (1). The automation system (1) processes the measurement data (M) and the control commands (S) when determining the control data (S′). An artificial intelligence unit (9) receives at least part of the measurement data (M), control data (S′) and/or internal data (I) and the data output to the person (4). It also receives the control commands (S). The artificial intelligence unit (9) processes the data (M, S′, I) and control demands (S) received and determines evaluation results (A) therefrom and makes the latter available to the person (4) and/or to the production planning system (11) and/or sets them for the automation system (1) in the form of control commands (5″) directly or via the man-machine interface (3). The data (M, S′, I) received by the artificial intelligence unit (9) are at least to some extent dimensional data. Said dimensional data (M, S′, I) comprise at least one image captured by a sensor device (2) or an image output via the man-machine interface (3), part of such an image, a time sequence of such images or a time sequence of a part of such images or an acoustic oscillation or an acoustic oscillation spectrum.

Provided is a defect occurrence prediction system for a machine tool that makes it possible to identify the factors causing the occurrence of defects efficiently and effectively, and predict the occurrence of the defects accurately with good precision. A defect occurrence prediction system includes an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool; a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time.

The present disclosure provides a system and a method for controlling a semiconductor manufacturing apparatus. The system includes an inspection unit capturing at least one image of a wafer, a sensor interface generating at least one input signal for a database server, and a control unit. The control unit includes a front-end subsystem, a calculation subsystem, and a message and tuning subsystem. The front-end subsystem receives the at least one input signal from the database server and performs a front-end process to generate a data signal. The calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether damage marks have been caused by the semiconductor manufacturing apparatus and to generate an output signal. The message and tuning subsystem generates an alert signal and a feedback signal according to the output signal and transmits the alert signal to a user.

A device and method to move user interacting devices responsive to user input using artificial intelligence. The device includes a movable mount for supporting the user interacting device. User input is converted into service requests using artificial intelligence services. The device converts the service requests into movement commands, which it may then execute. The user interacting device may receive and process user input into service requests or the devices itself may be configured with artificial intelligence services. The device then converts into movement commands by imparting motion on the movable base through use of one or more motors or other movement generating devices.