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.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

A control device for performing optimal control by path integral includes a neural network section including a machine-learned dynamics model and cost function, an input section that inputs a current state of a control target and an initial control sequence for the control target into the neural network section, and an output section that outputs a control sequence for controlling the control target, the control sequence being calculated by the neural network section by path integral from the current state and the initial control sequence by using the dynamics model and the cost function. Here, the neural network section includes a second recurrent neural network incorporating a first recurrent neural network including the dynamics model.

A numerical controller has a machine learning device that performs machine learning of the adjustment of a setting value used in override control. The machine learning device acquires state data showing states of the numerical controller and a machine, sets reward conditions, calculates a reward based on the state data and the reward conditions, performs the machine learning of the adjustment of the setting value used in override control, and determines the adjustment of the setting value used in override control, based on a machine learning result and the state data.

To perform reinforcement learning that enables selecting action information for shortening a cycle time while also avoiding the occurrence of overheating. An action information learning device (300) includes: a state information acquisition means (310) for acquiring state information including an operation pattern of a spindle and a combination of parameters related to machining of a machine tool (100); an action information output means (320) for outputting action information including adjustment information for the operation pattern and the combination of parameters included in the state information; a reward calculation means (333) for acquiring judgment information which is information for temperature of the machine tool (100) and a machining time related to the machining of the machine tool (100), and calculating a value of a reward for reinforcement learning based on the judgment information thus acquired; and a value function update means (332) for updating a value function by performing the reinforcement learning based on the value of the reward, the state information and the action information.

Embodiments of the disclosure provide for intelligent model integration within a process simulation system. Some embodiments receive data associated with the operation of a plant, determine, using at least one specially configured algorithm and based on the received data, at least one qualifying dataset determined qualified to train an intelligent model, train the intelligent model using the at least one qualifying dataset, and deploy the trained intelligent model for use.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.