Abstract: The observation update unit (152) increases a weight of a particle for a particle representing a position and a posture closer to a position and a posture of an operation object indicated by an observation result, and increases a weight of a corresponding particle for a particle closer to a state in which an object of interest and the operation object arranged in a position and a posture represented by each of particles are in contact with each other in a virtual space where shapes and a relative positional relationship of the object of interest and the operation object are expressed in a case in which the observation result indicates that the contact has occurred. A state estimation unit (154) calculates an estimated state that is a position and a posture of the operation object estimated based on a set of the particles in which a weight of each of the particles is adjusted.

Abstract: Provided is an image determination device. The image determination device is provided with: feature extractors which output, on the basis of an image to be examined, each piece of feature data indicating a specific feature of the image; a determiner which outputs, on the basis of the feature data output from the extractors, output data indicating the determination result pertaining to the image; and a training part which trains the determiner so as to output, output data indicating the label data associated with the training image on the basis of the feature data output when the training image is input to the extractors, wherein the training part further trains, by using new training data, the determiner so that the output data indicating the label data associated with the image is output by the determiner.

Abstract: An image determination device includes feature extractors which output, on the basis of an image to be examined, each piece of feature data indicating a specific feature of the image; a first training part which trains a first determiner so as to output first output data indicating first label data associated with a first training image on the basis of first feature data output when the first training image is input to the feature extractors; a second training part which trains a second determiner so as to output second output data indicating second label data associated with a second training image on the basis of second feature data output when the second training image is input to the feature extractors; and an output part which outputs, on the basis of the first output data and the second output data, output data indicating an overall determination result about an image.

Abstract: An image determination device includes: a training model which outputs, on the basis of an image to be examined, output data indicating a determination result about the image; a training part which trains the training model to output, by using training data including a training image and label data, output data indicating the label data associated with the training image, when the training image is input to the training model; a dividing part which divides the training data into a plurality of pieces of sub-training data; a measurement part which measures accuracy of determination when the training part trains the training model by using each of the plurality of pieces of sub-training data; and selection part which selects at least any one among the plurality of pieces of sub-training data on the basis of the accuracy of determination.

Abstract: An image determination device includes: a training model which outputs, on the basis of an image to be examined, output data indicating a determination result about the image; a training part which trains the training model to output, by using training data including a training image and label data, output data indicating the label data associated with the training image, when the training image is input to the training model; a dividing part which divides the training data into a plurality of pieces of sub-training data; a measurement part which measures accuracy of determination when the training part trains the training model by using each of the plurality of pieces of sub-training data; and selection part which selects at least any one among the plurality of pieces of sub-training data on the basis of the accuracy of determination.

Abstract: The image processing device is provided with: a first input unit which, with respect to one or more virtual models including a virtual model of an operation machine, receives an input of a first parameter for identifying a type; a second input unit which receives an input of a second parameter relating to a stochastic distribution having, as a random variable, a characteristic of an element constituting the one or more virtual models; a virtual model generation unit which, using the first parameter and the second parameter, generates the one or more virtual model stochastically; a determination unit which determines the correctness of an operation of the virtual model of the operation machine when operated in a virtual space including the one or more stochastically generated virtual models; and a learning unit which learns a control module for the operation machine for achieving a predetermined operation.

Abstract: A control device sets a relative relation amount between a plurality of target objects that are to be final objectives, repeatedly acquires observation data from a sensor and calculates the relative relation amount between the plurality of target objects existing in the environment from the acquired observation data. Further, the control device determines a series of the relative relation amounts in a state that is to be an objective from the relative relation amount at a time point at which control of the behavior starts until the relative relation amount as the final objectives is realized, and repeatedly determines control instructions so as to change the relative relation amount in a present state calculated from the latest observation data into the relative relation amount in a state of an objective to be transitioned to next. Then, the control device outputs the determined control instruction to a robot device.

Abstract: A control apparatus according to one or more embodiments may calculate a first estimate value of the coordinates of an endpoint of a manipulator based on first sensing data obtained from a first sensor system, calculates a second estimate value of the coordinates of the endpoint of the manipulator based on second sensing data obtained from a second sensor system, and adjust a parameter value for at least one of a first estimation model or a second estimation model to reduce an error between the first estimate value and the second estimate value based on a gradient of the error.

Abstract: A model generation apparatus according to one or more embodiments may include: a data obtainer configured to obtain a plurality of learning datasets each including a combination of training data and true data, the training data indicating a positional relationship between two objects, the true data indicating whether the two objects come in contact with each other in the positional relationship; and a machine learning unit configured to train, through machine learning, a determination model using the obtained plurality of learning datasets to cause the determination model to output, in response to an input of training data included in each of the plurality of learning datasets, an output value fitting true data included in a corresponding learning dataset of the plurality of learning datasets.

Abstract: A path planning apparatus is provided with a path planning unit that generates a path of a robot using a plurality of different path planning methods that respectively correspond to a plurality of different constraints determined from the posture of the robot and the characteristics of one or more obstacles that obstruct movement of the robot, an acquisition unit that acquires posture information indicating an initial posture of a robot for which a path is to be generated and a target posture of the robot, and obstacle information indicating a target obstacle that obstructs movement of the robot from the initial posture to the target posture, and a controller that controls the path planning unit so as to generate a path of the robot using a path planning method corresponding to a constraint determined from the posture information and the obstacle information acquired by the acquisition unit.

Abstract: Provided is an image generation device capable of generating, on the basis of inputted images, a learning image for training an action of a robot which carries out a prescribed operation on a workpiece. The image generation device comprises: a first image acquisition unit which acquires a first image capturing a real operation space including the robot and not including the workpiece; a second image acquisition unit which acquires a second image in which a virtual operation space including a virtual robot corresponding to the robot and a virtual workpiece corresponding to the workpiece is rendered; and a learning apparatus so that, in response to an input of the first image and the second image, the apparatus outputs a third image obtained by transforming the second image such that at least the virtual robot included in the second image is made to approximate the robot included in the first image.

Abstract: A robot control device is provided to accept information (201) specifying an object 170) manipulated by a robot (20) from among objects of a plurality of kinds and accept information (202) specifying a target relative positional relationship between the specified object (70) and the distal end of a hand of the robot (20). The robot control device extracts the object (70) from image information (501) obtained by photographing the objects of the plurality of kinds and the surrounding environment thereof, generates information (301) indicating the position and orientation of the object (70), generates an action instruction (401) from the result of learning by a learning module (103), the action instruction (401) serving to match the relative positional relationship between the object (70) and the distal end of the hand of the robot (20) with the target relative positional relationship, and outputs the action instruction (401) to the robot (20).

Abstract: A robot control device 10 is provided with: a reading device 40 that reads a marker 21 attached to a robot 20 and markers 31 attached to individual objects 30; and a CPU 111 that performs image analysis concerning the individual positions of the robot 20 and the individual objects 30 in real space based on information read from the markers 21, 31, and that simulates the operation of the robot 20 while disposing three-dimensional shape models of the robot 20 and the individual objects 30 in a virtual space based on information indicating the positions of the robot 20 and the individual objects 30 in real space, information concerning the three-dimensional shape model of the robot 20, associated with the marker 21, and information concerning the three-dimensional shape models of the individual objects 30, associated with the individual markers 31.

Abstract: Provided is an image determination device. The image determination device is provided with: feature extractors which output, on the basis of an image to be examined, each piece of feature data indicating a specific feature of the image; a determiner which outputs, on the basis of the feature data output from the extractors, output data indicating the determination result pertaining to the image; and a training part which trains the determiner so as to output, output data indicating the label data associated with the training image on the basis of the feature data output when the training image is input to the extractors, wherein the training part further trains, by using new training data, the determiner so that the output data indicating the label data associated with the image is output by the determiner.

Abstract: An image determination device includes: a training model which outputs, on the basis of an image to be examined, output data indicating a determination result about the image; a training part which trains the training model to output, by using training data including a training image and label data, output data indicating the label data associated with the training image, when the training image is input to the training model; a dividing part which divides the training data into a plurality of pieces of sub-training data; a measurement part which measures accuracy of determination when the training part trains the training model by using each of the plurality of pieces of sub-training data; and selection part which selects at least any one among the plurality of pieces of sub-training data on the basis of the accuracy of determination.

Abstract: A control device sets a relative relation amount between a plurality of target objects that are to be final objectives, repeatedly acquires observation data from a sensor and calculates the relative relation amount between the plurality of target objects existing in the environment from the acquired observation data. Further, the control device determines a series of the relative relation amounts in a state that is to be an objective from the relative relation amount at a time point at which control of the behavior starts until the relative relation amount as the final objectives is realized, and repeatedly determines control instructions so as to change the relative relation amount in a present state calculated from the latest observation data into the relative relation amount in a state of an objective to be transitioned to next. Then, the control device outputs the determined control instruction to a robot device.

Abstract: An image determination device includes feature extractors which output, on the basis of an image to be examined, each piece of feature data indicating a specific feature of the image; a first training part which trains a first determiner so as to output first output data indicating first label data associated with a first training image on the basis of first feature data output when the first training image is input to the feature extractors; a second training part which trains a second determiner so as to output second output data indicating second label data associated with a second training image on the basis of second feature data output when the second training image is input to the feature extractors; and an output part which outputs, on the basis of the first output data and the second output data, output data indicating an overall determination result about an image.

Abstract: A motion generation device may be for generating a movement for changing the robot from a first orientation to a second orientation, and include a first acquisition unit that acquires first orientation information that specifies the first orientation and second orientation information that specifies the second orientation, a second acquisition unit that acquires at least one priority item regarding the movement for changing from the first orientation to the second orientation, and a movement generation unit that generates a motion of the robot that includes a movement path along which the robot moves from the first orientation to the second orientation, based on the first orientation information, the second orientation information, and the priority item that were acquired.

Abstract: A control device assumes that observation light observed by an imaging device is composite light of primary reflection light and secondary reflection light. The control device acquires three or more samples of a brightness amplitude value of the observation light, calculates a phase error caused by the secondary reflection light using these samples, calculates a corrected phase value by correcting a phase value of the observation light using the phase error, and calculates a three-dimensional position of the measurement point on the measurement object based on the corrected phase value.

Abstract: Provided are a simulation device, a simulation method, and a computer-readable storage medium, which reduce a computation load required for simulation of movement of an operation subject. This simulation device is provided with: a first setting unit that sets framing conditions for a model representing the operation subject; a second setting unit that sets conditions for external force applied to the operation subject; a first simulation unit that simulates the movement of the operation subject under the framing conditions and the conditions for external force; a generation unit that generates learning data; and a learning unit that, by supervised learning using the learning data, generates a learning model that takes the framing conditions, the conditions for external force, and the initial conditions of the plurality of representative points as input and outputs data representing the movement of the plurality of representative points.