A machine learning device performs reinforcement learning on a controller that performs multiple processes for controlling a machine tool in parallel at multiple operation units. The machine learning device comprises: behavior information output means that outputs behavior information containing allocation of arithmetic units that perform the multiple processes to the controller; state information acquisition means that acquires state information containing a machining condition as a condition for machining set at the machine tool, and determination information generated by monitoring the implementation of the multiple processes by the multiple operation units based on the allocation in the behavior information; reward calculation means that calculates the value of a reward to be given by the reinforcement learning based on the determination information in the state information; and value function update means that updates a behavior value function based on the reward value, the state information, and the behavior information.

A controller for a machine tool includes a machine learning apparatus configured to learn an Nth-order time-derivative component of a speed of each axis of the machine tool. The machine learning apparatus includes: a state observation section configured to observe first state data representing the Nth-order time-derivative component of the speed of each axis as a state variable representing a current state of an environment; a determination data acquisition section configured to acquire determination data representing a properness determination result of at least any one of machining accuracy, surface quality, and machining time of the machined workpiece; and a learning section configured to learn the Nth-order time-derivative component of the speed of each axis in relation to at least any one of the machining accuracy, the surface quality, and the machining time of the machined workpiece using the state variable and the determination data.

To provide an action information learning device, robot control system and action information learning method for facilitating the performing of cooperative work by an operator with a robot. An action information learning device includes: a state information acquisition unit that acquires a state of a robot; an action information output unit for outputting an action, which is adjustment information for the state; a reward calculation section for acquiring determination information, which is information about a handover time related to handover of a workpiece, and calculating a value of reward in reinforcement learning based on the determination information thus acquired; and a value function update section for updating a value function by way of performing the reinforcement learning based on the value of reward calculated by the reward calculation section, the state and the action.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an optical parameter related to an optical system imaging a target object, by using machine learning, detect the target object on the basis of an imaging result in the optical system by using the calculated optical parameter, and control a robot on the basis of a detection result of the target object.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an operation parameter related to an operation of a robot by using machine learning, and control the robot on the basis of the calculated operation parameter.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate an image processing parameter related to image processing on an image of a target object captured by a camera, by using machine learning, detect the target object on the basis of an image on which the image processing is performed by using the calculated image processing parameter, and control a robot on the basis of a detection result of the target object.

A control device includes a processor that is configured to execute computer-executable instructions so as to control a robot, wherein the processor is configured to calculate a force control parameter related to force control of a robot by using machine learning, and control the robot on the basis of the calculated force control parameter.

A system and method of instructing a device is disclosed. The system includes a signal source for providing at least one visual signal where the at least one visual signal is substantially indicative of at least one activity to be performed by the device. A visual signal capturing element captures the at least one visual signal and communicates the at least one visual signal to the device where the device interprets the at least one visual signal and performs the activity autonomously and without requiring any additional signals or other information from the signal source.

Provided are a controller and a machine learning device that perform machine learning to optimize the servo gain of a machine inside a facility in accordance with action conditions, action environments, and a priority factor of the machine. Disclosed is a control system including: a state observation section that observes machine information on a machine as state data; a determination data acquisition section that acquires information on machining by a machine as determination data; a reward calculation section that calculates a reward based on the determination data and reward conditions; a learning section that performs the machine learning of the adjustment of the servo gain of the machine; a decision making section that determines an action of adjustment of the servo gain of the machine, based on the state data and a machine learning result of the adjustment of the servo gain of the machine; and a gain changing section that changes the servo gain of the machine, based on the action of adjustment of the determined servo gain.

A numerical controller includes a machine learning device for performing machine learning of machining condition adjustment of a machine tool. The machine learning device calculates a reward based on acquired machining-state data on a workpiece, and determines an adjustment amount of machining condition based on a result of machine learning and machining-state data, and adjusts machining conditions based on the adjustment amount. Further, the machine learning of machining condition adjustment is performed based on the determined adjustment amount of machining condition, the machining-state data, and the reward.