A picture processing method for laser machining includes: obtaining picture generation information for generating a target picture required by a user; where the picture generation information includes at least picture description information; processing the picture generation information by a picture generation model to obtain a target picture consistent with the picture generation information; and importing the target picture into a laser machining process of a laser machining device.

Systems and methods for controlling robots including industrial robots. A method includes executing (402) a program (550) to control a robot (102) by the robot control system (120, 500). The method includes receiving (404) robot state information (554). The method includes receiving (406) force torque feedback (556) inputs from a sensor (554) on the robot (102). The method includes producing (410) a robot control command for the robot (102) based on the robot state information (554) and the force torque feedback (556) inputs. The method includes controlling (412) the robot (102) using the robot control command.

A computer-implemented method for optimizing a numerical control of a machine tool having a tool for machining a workpiece may include obtaining numerical tool information with respect to a machining. The method may further include acquiring, by a trained neural network, timing information based on the obtained tool information and generating a set of path information for machining from the timing information and the tool information. The set of path information may include a travel path of the tool, which may include an approach, tool entry, working, and tool exit phase, and a distance between the tool and the workpiece before the approach phase. The method may include determining a minimum distance between the tool and the workpiece before the approach phase from a plurality of sets of path information of a plurality of previous machinings for a next machining.

A method for training a machine-learning model to assess at least one condition of industrial equipment, and/or at least one condition of a process running in an industrial plant, based on measurement data gathered by a plurality of sensors, includes: obtaining a plurality of records of measurement data that correspond to a variety of operating situations and a variety of conditions; obtaining, for each record of measurement data, a label that represents a condition in the operating situation characterized by the record of measurement data; and determining a plausibility of at least one record of measurement data, and/or a plausibility of at least one label, based at least in part on a comparison with at least one other record of measurement data, with at least one other label, and/or with additional information about the industrial equipment, and/or about the industrial plant where the industrial equipment resides, and/or about the process.

A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector for the robot to insert an object into an insertion. The method includes controlling the robot to hold the object, bringing the robot into a target position in which the object is inserted in the insertion, for a plurality of positions different from the target position controlling the robot to move away from the target position to the position, taking a camera image by the camera and labelling the camera image by a movement vector to move back from the position to the target position and training the neural network using the labelled camera images.

A method for changing a component configuration of an apparatus includes: obtaining global change information and state information, the state information corresponding to a current time step, of an environment in which the apparatus is located; obtaining latent features corresponding to candidate actions, based on the state information and based on the global change information; determining candidate action probability values respectively corresponding to the candidate actions, based on the latent features corresponding to the candidate actions; determining a target action based on the candidate action probability values; and changing the component configuration based on the target action.

A method determines state information relating to a belt grinder. The belt grinder has at least one abrasive belt for grinding a workpiece. The method includes providing measurement data relating to the belt grinder, and determining the state information from the measurement data using a machine learning system. The machine learning system is configured to determine the state information based on the provided measurement data.

A robot control apparatus can include a memory that stores computer-executable instructions and at least one processor that executes the instructions by accessing the memory. The at least one processor can apply event information about activity of a user, which can be identified from user activity data perceived from a robot, and context information about time and space in which the activity occurs, to a knowledge graph formed by a relation between an event instance regarding the event information and a context instance regarding the context information, obtain user intent data regarding intent of the activity by applying the event instance among instances included in the knowledge graph to a rule creation model for creating information about the intent of the activity, and control the robot such that the robot performs a target task related to expected activity, which can follow the activity, based on the user intent data.

A first selection of a first fabrication process and/or first manufacturing equipment to perform manufacturing operations of the first fabrication process is received. The first selection is input into a digital replica of the first manufacturing equipment, where the digital replica outputs physical conditions of the first fabrication process. Environmental resource usage data indicative of a first environmental resource consumption of the first fabrication process run on the first manufacturing equipment based on the physical conditions of the first fabrication process is determined. A modification to the first fabrication process that reduces the environmental resource consumption of the first fabrication process run on the first manufacturing equipment is determined. Applying the modification to the first fabrication and/or providing the modification for display by a graphical user interface (GUI) is performed.

A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.