Abstract: A robot control system includes circuitry configured to: determine a necessity of assisting a robot to complete an automated work, based on environment information of the robot; select a remote operator from candidate remote operators based on stored operator data in response to determining that it is necessary to assist the robot to complete the automated work; transmit the environment information to the selected remote operator via a communication network; receive an operation instruction based on the environment information from the selected remote operator via the communication network; and control the robot to complete the automated work based on the operation instruction.

Abstract: An event estimation system includes an upper device, and a lower controller device including first circuitry that acquires operation information of a control target device connected to the lower controller device, estimates a presence or absence of an abnormality based on the operation information, holds the operation information for a certain time period, and transmits, based on the presence or absence of an abnormality and to the upper device, the operation information related to the estimation of the presence or absence of the abnormality. The upper device has second circuitry that receives the operation information from the lower controller device, and operates according to the presence or absence of the abnormality, inputs, using an upper neural network model, the operation information, output event information, and estimates an event.

Abstract: A robot control system includes circuitry configured to: generate a command to a robot; receive a frame image in which a capture position changes according to a motion of the robot based on the command; extract a partial region from the frame image according to the command; superimpose a delay mark on the partial region to generate an operation image; and display the operation image on a display device, so as to represent a delay of the motion of the robot with respect to the command.

Abstract: Provided is a machine learning model determination system including: at least one server and at least one client terminal; an evaluation information database which stores evaluation information being information on an evaluation of machine learning; an evaluation information update module which updates the evaluation information based on a specific value of a parameter and an evaluation of the machine learning through use of specific teaching data; a teaching data input module; a verification data input module; a parameter determination module which determines the specific value of the parameter based on the evaluation information; and a machine learning engine which includes a learning module which executes learning for a machine learning model through use of the specific teaching data, and an evaluation module which evaluates a result of the machine learning through use of the specific verification data.

Abstract: A robot control system includes circuitry configured to: acquire an input command value indicating a manipulation of a robot by a subject user; acquire a current state of the robot and a target state associated with the manipulation of the robot; determine a state difference between the current state and the target state; acquire from a learned model, a degree of distribution associated with a motion of the robot, based on the state difference, wherein the learned model is generated based on a past robot manipulation; set a level of assistance to be given during the manipulation of the robot by the subject user, based on the degree of distribution acquired; and generate an output command value for operating the robot, based on the input command value and the level of assistance.

Abstract: To generate a more appropriate path, provided is a robot path generation device including circuitry configured to: hold a track planning module learning data set, in which a plurality of pieces of path data generated based on a motion constraint condition of a robot, and evaluation value data, which corresponds to each of the plurality of pieces path data and is a measure under a predetermined evaluation criterion, are associated with each other; and generate, based on a result of a machine learning process that is based on the track planning module learning data set, a path of the robot between a set start point and a set end point, which are freely set.

Abstract: A teaching apparatus includes circuitry. The circuitry is configured to obtain result information corresponding to a position of a worked region on a workpiece. The circuitry is configured to generate first teaching information based on the result information. The first teaching information specifies a motion of an examination robot configured to examine the workpiece that has undergone work.

Abstract: A gripping system includes a hand that grips a workpiece, a robot that supports the hand and changes at least one of a position and a posture of the hand, and an image sensor that acquires image information from a viewpoint interlocked with at least one of the position and the posture of the hand. Additionally, the gripping system includes a construction module that constructs a model by machine learning based on collection data. The model corresponds to at least a part of a process of specifying an operation command of the robot based on the image information acquired by the image sensor and hand position information representing at least one of the position and the posture of the hand. An operation module executes the operation command of the robot based on the image information, the hand position information, and the model, and a robot control module operates the robot based on the operation command of the robot operated by the operation module.

Abstract: An inspection system includes machine learning circuitry configured to determine whether each of objects belongs to a predetermined attribute based on feature data of each of the objects, feature data acquisition circuitry configured to acquire feature data of reevaluated objects which are determined to belong to the predetermined attribute without using the machine learning circuitry among excluded objects which are determined not to belong to the predetermined attribute by the machine learning circuitry, and parameter update circuitry configured to update a learning parameter of the machine learning circuitry based on teaching data including the acquired feature data acquired by the feature data acquisition circuitry.

Abstract: A robot control system includes circuitry configured to: determine a necessity of assisting a robot to complete an automated work, based on environment information of the robot; select a remote operator from candidate remote operators based on stored operator data in response to determining that it is necessary to assist the robot to complete the automated work; transmit the environment information to the selected remote operator via a communication network; receive an operation instruction based on the environment information from the selected remote operator via the communication network; and control the robot to complete the automated work based on the operation instruction.

Abstract: An image recognition processor for an industrial device, the image recognition processor implementing, on an integrated circuit thereof, the functions of storing an image data processing algorithm, which has been determined based on prior learning; acquiring image data of an image including a predetermined pattern; and performing recognition processing on the image data based on the image data processing algorithm to output identification information for identifying a recognized pattern.

Abstract: An event estimation system includes an upper device, and a lower controller device including first circuitry that acquires operation information of a control target device connected to the lower controller device, estimates a presence or absence of an abnormality based on the operation information, holds the operation information for a certain time period, and transmits, based on the presence or absence of an abnormality and to the upper device, the operation information related to the estimation of the presence or absence of the abnormality. The upper device has second circuitry that receives the operation information from the lower controller device, and operates according to the presence or absence of the abnormality, inputs, using an upper neural network model, the operation information, output event information, and estimates an event.

Abstract: To generate a more appropriate path, provided is a robot path generation device including circuitry configured to: hold a track planning module learning data set, in which a plurality of pieces of path data generated based on a motion constraint condition of a robot, and evaluation value data, which corresponds to each of the plurality of pieces path data and is a measure under a predetermined evaluation criterion, are associated with each other; and generate, based on a result of a machine learning process that is based on the track planning module learning data set, a path of the robot between a set start point and a set end point, which are freely set.

Abstract: An image recognition processor for an industrial device, the image recognition processor implementing, on an integrated circuit thereof, the functions of storing an image data processing algorithm, which has been determined based on prior learning; acquiring image data of an image including a predetermined pattern; and performing recognition processing on the image data based on the image data processing algorithm to output identification information for identifying a recognized pattern.

Abstract: A gripping system includes a hand that grips a workpiece, a robot that supports the hand and changes at least one of a position and a posture of the hand, and an image sensor that acquires image information from a viewpoint interlocked with at least one of the position and the posture of the hand. Additionally, the gripping system includes a construction module that constructs a model by machine learning based on collection data. The model corresponds to at least a part of a process of specifying an operation command of the robot based on the image information acquired by the image sensor and hand position information representing at least one of the position and the posture of the hand. An operation module executes the operation command of the robot based on the image information, the hand position information, and the model, and a robot control module operates the robot based on the operation command of the robot operated by the operation module.

Abstract: A teaching apparatus includes circuitry. The circuitry is configured to obtain result information corresponding to a position of a worked region on a workpiece. The circuitry is configured to generate first teaching information based on the result information. The first teaching information specifies a motion of an examination robot configured to examine the workpiece that has undergone work.

Abstract: A work planner includes a divider and an adjustor. Based on action information including a plurality of first actions indicating work units that involve one executor or a plurality of executors including a robot and that start at respective defined start timings, the divider is configured to divide one action among the plurality of first actions that involves the plurality of executors into a plurality of second actions corresponding to the respective plurality of executors. Based on dependency information indicating a relationship of dependency among the plurality of first actions including the plurality of second actions, the adjustor is configured to adjust a start timing of at least one of third actions, among the plurality of first actions, that involve an identical executor.

Abstract: A work planner includes a divider and an adjustor. Based on action information including a plurality of first actions indicating work units that involve one executor or a plurality of executors including a robot and that start at respective defined start timings, the divider is configured to divide one action among the plurality of first actions that involves the plurality of executors into a plurality of second actions corresponding to the respective plurality of executors. Based on dependency information indicating a relationship of dependency among the plurality of first actions including the plurality of second actions, the adjustor is configured to adjust a start timing of at least one of third actions, among the plurality of first actions, that involve an identical executor.

Abstract: In the wafer position teaching method for a wafer carrying system, a teaching tool is mounted at a position of the container or the processing equipment where the semiconductor wafer is to be set. The teaching tool is sensed by a sensor provided at a wafer gripping portion of the robot. Prior to sensing the teaching tool by the sensor, external teaching tools mounted on a front external wall of the processing equipment are sensed by the sensor to roughly estimate the position of the teaching tool. Based on the estimated position, the sensor approaches and senses the teaching tool to obtain the position of the semiconductor wafer. Thus, the wafer position can be taught precisely and automatically without causing interference, even when the frontage of processing equipment is narrow.

Abstract: The present invention provides a non-linear distortion detection method and a distortion compensation amplifying device capable of suppressing increase of the circuit size and the power consumption even if the signal band is widened. A signal obtained by feeding back an output of a power amplifier is sampled by an A/D converter. An equalizer of a distortion detection unit uses an input signal d(n) of a predistorter as a reference symbol to detect an equalization error e(n) of the orthogonal demodulation signal u(n). An absolute value averaging unit outputs an absolute value of the equalization error e(n) which has been temporally averaged to E(n) as a distortion value to a control unit. According to the distortion value, the control unit adaptively controls the predistorter to perform distortion compensation.