Abstract: The disclosure is to constitute, while reducing a cost for collecting training data used in machine learning that makes a control module acquire an ability to control a robot device, the control module operatable in an actual environment by the machine learning. A learning device according to one aspect of the present invention executes machine learning of an extractor by using a first learning data set constituted by a combination of simulation data and first environmental information and a second learning data set constituted by a combination of actual data and second environmental information. Further, a learning device according to one aspect of the present invention executes machine learning of a controller by using a third learning data set constituted by a combination of third environmental information, state information, and a control command.

Abstract: A joint structure includes a locking mechanism for switching a free state in which a second element is independent from a first element and capable of moving, and a locked state in which the second element is fixed to the first element. The locking mechanism includes a first member joined to the first element, a second member joined to the second element, and a flexible wire-shaped member in which one end thereof is attached to the second member and another end thereof is led out to the outside of the joint structure via a through hole provided in the first member. The joint structure enters the locked state by the wire-shaped member being pulled to bring the second member into contact with the first member, and enters the free state by the wire-shaped member being fed to separate the second member from the first member.

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 apparatus of a robot may include a state obtaining unit configured to obtain state observation data including flexible related observation data, which is observation data regarding a state of at least one of a flexible portion, a portion of the robot on a side where an object is gripped relative to the flexible portion, and the gripped object; and a controller configured to control the robot so as to output an action to be performed by the robot to perform predetermined work on the object, in response to receiving the state observation data, based on output obtained as a result of inputting the state observation data obtained by the state obtaining unit to a learning model, the learning model being learned in advance through machine learning and included in the controller.

Abstract: This control device for controlling the motion of a robot comprises a first processing part and a command part. The first processing part sets a first state of the robot and a second state to which the robot transitions from the first state as inputs, and sets at least one basic motion selected from a plurality of basic motions the robot is instructed to perform for transitioning from the first state to the second state and the order in which the basic motions are to be performed as outputs. Prescribed operating parameters are set for each of the basic motions. The command part executes motion commands for the robot on the basis of the output from the first processing part.

Abstract: A control apparatus of a robot that includes a flexible portion and a locking mechanism for fixing the flexible portion, the control apparatus including: a lock control unit configured to control locking and unlocking of the flexible portion; and an operation control unit configured to control operation of the robot using different types of control policies depending on whether the flexible portion is locked or unlocked. With this control apparatus, it is possible to effectively control a robot in which locking and unlocking of the flexible portion can be switched.

Abstract: A joint structure includes a locking mechanism for switching a free state in which a second element is independent from a first element and capable of moving, and a locked state in which the second element is fixed to the first element. The locking mechanism includes a first member joined to the first element, a second member joined to the second element, and a flexible wire-shaped member in which one end thereof is attached to the second member and another end thereof is led out to the outside of the joint structure via a through hole provided in the first member. The joint structure enters the locked state by the wire-shaped member being pulled to bring the second member into contact with the first member, and enters the free state by the wire-shaped member being fed to separate the second member from the first member.

Abstract: A control apparatus causes a robot device to move a suction head to a predetermined position at which a workpiece is fed and attempt to pick up the workpiece with the suction head at the predetermined position. Upon determining that the suction head has yet to pick up the workpiece, the control apparatus causes the robot device to rotationally move the suction head spirally in a horizontal direction while causing the suction head to perform a suction operation for the workpiece, and estimates a direction in which the workpiece is located with respect to the predetermined direction based on a change in compressed air pressure during the rotational movement of the suction head.

Abstract: A program generation apparatus according to one or more embodiments may extract, from a series of motions defined in a motion program, a motion to be corrected based on a difference in attribute between a first component indicated as a target of a component change and a second component to replace the first component, and generate a new motion program by correcting a command value of the extracted motion to compensate for the difference in the attribute.

Abstract: An operation control device for a robot comprises: an input part inputting at least one operation candidate, and a captured image including an object to be processed; a first learning device that has finished learning performed according to first learning data to output a first evaluation value indicating evaluation of each operation candidate when the robot performs a first processing operation upon input of the captured image and the operation candidate; a second learning device that has finished learning performed according to second learning data which differs from the first learning data, to output a second evaluation value indicating evaluation of each operation candidate when the robot performs a second processing operation upon input of the captured image and the operation candidate; and an evaluation part that, based on at least one of the first evaluation value and the second evaluation value, calculates a command value.

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: 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 control apparatus of a robot may include a state obtaining unit configured to obtain state observation data including flexible related observation data, which is observation data regarding a state of at least one of a flexible portion, a portion of the robot on a side where an object is gripped relative to the flexible portion, and the gripped object; and a controller configured to control the robot so as to output an action to be performed by the robot to perform predetermined work on the object, in response to receiving the state observation data, based on output obtained as a result of inputting the state observation data obtained by the state obtaining unit to a learning model, the learning model being learned in advance through machine learning and included in the controller.

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.

Abstract: The disclosure is to constitute, while reducing a cost for collecting training data used in machine learning that makes a control module acquire an ability to control a robot device, the control module operatable in an actual environment by the machine learning. A learning device according to one aspect of the present invention executes machine learning of an extractor by using a first learning data set constituted by a combination of simulation data and first environmental information and a second learning data set constituted by a combination of actual data and second environmental information. Further, a learning device according to one aspect of the present invention executes machine learning of a controller by using a third learning data set constituted by a combination of third environmental information, state information, and a control command.

Abstract: An operation control device for a robot comprises: an input part inputting at least one operation candidate, and a captured image including an object to be processed; a first learning device that has finished learning performed according to first learning data to output a first evaluation value indicating evaluation of each operation candidate when the robot performs a first processing operation upon input of the captured image and the operation candidate; a second learning device that has finished learning performed according to second learning data which differs from the first learning data, to output a second evaluation value indicating evaluation of each operation candidate when the robot performs a second processing operation upon input of the captured image and the operation candidate; and an evaluation part that, based on at least one of the first evaluation value and the second evaluation value, calculates a command value.

Abstract: This control device for controlling the motion of a robot comprises a first processing part and a command part. The first processing part sets a first state of the robot and a second state to which the robot transitions from the first state as inputs, and sets at least one basic motion selected from a plurality of basic motions the robot is instructed to perform for transitioning from the first state to the second state and the order in which the basic motions are to be performed as outputs. Prescribed operating parameters are set for each of the basic motions. The command part executes motion commands for the robot on the basis of the output from the first processing part.

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.