A dental machining system for manufacturing a dental restoration/appliance, including: a dental tool machine which includes: a dental blank holder for movably holding at least one dental blank relative to one or more dental tools; one or more driving units each for movably holding one or more dental tools, a control unit for controlling the dental blank holder and the driving units based on construction data of the dental restoration/appliance and a plurality of machining processes specific for the manufacturing of the dental restoration/appliance from the dental blank. The control unite executes a trained artificial intelligence algorithm that predicts the machining time for manufacturing the dental restoration/appliance based on input data including: process parameters defining the machining processes respectively; and mappings which include information on the target geometry of the dental restoration/appliance.

A method for carrying out an application using at least one robot includes, repeatedly ascertaining a stochastic value of at least one robot parameter and/or at least one environmental model parameter; and carrying out a simulation of the application on the basis of the ascertained stochastic value, training at least one control agent and/or at least one classification agent using the simulations by machine learning, and carrying out the application using the robot. The method may further include configuring a controller of the robot, by means of which the application is carried out wholly or in part, based on the trained control agent, and/or classifying the application using the trained classification agent.

A machining condition adjustment device includes a data acquisition unit that acquires at least one piece of data indicating a state of machining including a machining type in a machine tool, a priority condition storage unit that stores priority condition data in which the machining type is associated with a priority condition, a preprocessing unit that produces data to be used for machine learning, and a machine learning device that carries out processing of the machine learning related to at least either of a machining condition and a machining parameter for machining by the machine tool. The machine learning device includes a learning model storage unit that stores a plurality of learning models generated for each machining type and a learning model selection unit that selects a learning model, based on the machining type.

Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.

Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.

Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.

Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s).

A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.

A machining condition adjustment device includes a data acquisition unit that acquires at least one piece of data indicating a state of machining including a machining type in a machine tool, a priority condition storage unit that stores priority condition data in which the machining type is associated with a priority condition, a preprocessing unit that produces data to be used for machine learning, and a machine learning device that carries out processing of the machine learning related to at least either of a machining condition and a machining parameter for machining by the machine tool. The machine learning device includes a learning model storage unit that stores a plurality of learning models generated for each machining type and a learning model selection unit that selects a learning model, based on the machining type.

The invention relates to a method and a computer-implemented system for automatically monitoring and determining the status of entire process sections in a process unit in a computer-implemented manner.