Apparatus and methods for training and controlling of e.g., robotic devices. In one implementation, a robot may be utilized to perform a target task characterized by a target trajectory. The robot may be trained by a user using supervised learning. The user may interface to the robot, such as via a control apparatus configured to provide a teaching signal to the robot. The robot may comprise an adaptive controller comprising a neuron network, which may be configured to generate actuator control commands based on the user input and output of the learning process. During one or more learning trials, the controller may be trained to navigate a portion of the target trajectory. Individual trajectory portions may be trained during separate training trials. Some portions may be associated with robot executing complex actions and may require additional training trials and/or more dense training input compared to simpler trajectory actions.

A controller includes data collect circuitry configured to collect machining data including a date and a time when at least one machined portion of a workpiece has been machined by a machine tool, temperature circuitry configured to obtain, at predetermined time intervals, temperature data at positions on the machine tool, dimension data input circuitry configured to receive dimension measurement data which includes a dimension of the machined portion after the machined portion has been machined, learning data generate circuitry configured to generate learning data based on the machining data and the dimension measurement data, and machine learning circuitry configured to execute a machine learning based on the temperature data and the learning data to obtain a correction coefficient based on which a displacement caused by a change in a temperature of the machine tool is corrected according to a thermal displacement correction equation.

Operating a substrate processing system includes receiving a plurality of sets of training data, storing a plurality of machine learning models, storing a plurality of physical process models, receiving a selection of a machine learning model from the plurality of machine learning models and a selection of a physical process model from the plurality of physical process models, generating an implemented machine learning model according to the selected machine learning model, calculating a characterizing value for each training spectrum in each set of training data thereby generating a plurality of training characterizing values with each training characterizing value associated with one of the plurality of training spectra, training the implemented machine learning model using the plurality of training characterizing values and plurality of training spectra to generate a trained machine learning model, and passing the trained machine learning model to a control system of the substrate processing system.

An NC device, which is a numerical control device, includes: a program analyzing unit that analyzes a machining program to obtain a movement path along which to move a supply position of a material on a workpiece; a storage temperature extracting unit that extracts, from data on surface temperature of the workpiece, storage temperature in an area including the movement path on the workpiece; a layering volume calculating unit that calculates a volume of a layer forming an object on the basis of a relation between the storage temperature and a volume of the material that solidifies at the storage temperature in a given time; and a layering shape changing unit that changes a shape of the layer on the basis of the volume of the layer.

The example systems, methods, and/or computer-readable media described herein help with design of highly accurate models of un-erupted or partially erupted teeth and help fabricate of aligners for un-erupted or partially erupted teeth. Automated agents that use machine learning models to parametrically represent three-dimensional (3d) virtual representations of teeth as 3D descriptors in a 3D descriptor space are provided herein. In some implementations, the automated agents described herein provide instructions to fabricate aligners for at least partially un-erupted teeth using representative 3D descriptor(s) of a tooth type.

A method including receiving a data packet over a network, the data packet having a size. The method also includes parsing the data packet into a header and a body. The method also includes identifying a protocol type from the header and the size. The method also includes identifying a signal characteristic of signal data in the body. The method also includes identifying a classification of a source sensor which generated the data packet based on the protocol type and the signal characteristic. The method also includes generating a metadata file based on the source sensor. The method also includes labeling the data packet with the metadata file to form a labeled data packet.

A power tool includes a housing and a sensor, a machine learning controller, a motor, and an electronic controller supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The machine learning controller includes a first processor and a first memory and is coupled to the sensor. The machine learning controller further includes a machine learning control program configured to receive the sensor data, process the sensor data using the machine learning control program, and generate an output based on the sensor data using the machine learning control program. The electronic controller includes a second processor and a second memory and is coupled to the motor and to the machine learning controller. The electronic controller is configured to receive the output from the machine learning controller and control the motor based on the output.

A set condition determining method for manufacturing facilities includes: inputting, into a trained model, a manufacturing condition for a target product and a setup condition that is for a product manufactured in same manufacturing facilities before manufacture of the target product and that reflects setup condition modification by an operator's manual manipulation; and obtaining a setup condition for the target product. The trained model has been trained with input being: manufacturing conditions for the target product; and setup conditions that are for the product manufactured in the same manufacturing facilities before the manufacture of the target product and that reflect setup condition modification by an operator's manual manipulation, and output being setup conditions for the target product.

The example systems, methods, and/or computer-readable media described herein help with design of highly accurate models of un-erupted or partially erupted teeth and help fabricate of aligners for un-erupted or partially erupted teeth. Automated agents that use machine learning models to parametrically represent three-dimensional (3d) virtual representations of teeth as 3D descriptors in a 3D descriptor space are provided herein. In some implementations, the automated agents described herein provide instructions to fabricate aligners for at least partially un-erupted teeth using representative 3D descriptor(s) of a tooth type.

A power tool includes a housing and a sensor, a machine learning controller, a motor, and an electronic controller supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The machine learning controller includes a first processor and a first memory and is coupled to the sensor. The machine learning controller further includes a machine learning control program configured to receive the sensor data, process the sensor data using the machine learning control program, and generate an output based on the sensor data using the machine learning control program. The electronic controller includes a second processor and a second memory and is coupled to the motor and to the machine learning controller. The electronic controller is configured to receive the output from the machine learning controller and control the motor based on the output.