According to embodiments, a robot system configured to determine a recipe from an ingredient list obtained by detecting a type and an amount of an ingredient includes: a storage table configured to detect a weight change by a scale installed under each of cells that store ingredients according to types of the ingredients, detect the cell of which a weight is changed according to an ingredient selection of a user to identify the type of the ingredient, and identify the amount of the ingredient based on a degree of the weight change of the cell; and a robot configured to receive the ingredient list obtained from the type and the amount of the ingredient identified by the storage table from the storage table, retrieve menus to be cooked with the ingredient list, and perform cooking by determining the recipe according to a menu selected from the menus.

A system includes an interface and a controller. The interface is configured to receive a state of a substrate processing tool comprising a plurality of process modules configured to process a substrate. The controller is configured to correlate the state with an input previously received by the substrate processing tool from the interface based on the state and to generate an output to control the substrate processing tool based on the correlation.

A machine-learning device performs machine-learning under machining conditions including at least a waiting time of laser emission for controlling machining of a subject to be machined in a laser machining apparatus, and comprises: an action output unit which selects, as an action, a machining condition from a plurality of machining conditions, and outputs the action to the laser machining apparatus; a state acquisition unit which acquires, as state information, image data obtained by imaging a machined state of the subject that has been machined by the action; a reward calculation unit which calculates a reward on the basis of the waiting time of the laser emission and the machining accuracy of the machining state calculated on the basis of at least the acquired state information; and a learning unit which performs machine-learning on the machining conditions on the basis of the acquired state information and the calculated reward.

A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.

A controller teaches a teaching point of a slave axis corresponding to a master axis so as to perform a synchronous operation. The controller calculates a teaching range based on one moving speed pattern selected from a plurality of moving speed patterns of the master axis which are preliminarily registered, a preliminarily-set allowable speed in an operation of the slave axis, and a calculated teaching range, in which teaching can be performed, of a following teaching point, so as to display the teaching range on a display device.

A robot control device includes: a trained model built by being trained on work data; a control data acquisition section which acquires control data of the robot based on data from the trained model; base trained models built for each of a plurality of simple operations by being trained on work data; an operation label storage section which stores operation labels corresponding to the base trained models; a base trained model combination information acquisition section which acquires combination information when the trained model is represented by a combination of a plurality of the base trained models, by acquiring a similarity between the trained model and the respective base trained models; and an information output section which outputs the operation label corresponding to each of the base trained models which represent the trained model.

A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.

A system includes an interface and a controller. The interface is configured to receive a state a substrate processing tool comprising a plurality of process modules configured to process a substrate. The controller is configured to correlate the state with an input previously received by the substrate processing tool from the interface based on the state and to generate an output to control the substrate processing tool based on the correlation.

A system for manufacturing a composite structure includes a tool with a surface for supporting component elements of the composite structure. The surface is divided into a multi-dimensional array of thermal zones for in-process control of the temperature of a component element (e.g. resin) of the composite structure. The sensors sense a characteristic of the component element and provide sensor data, which is applied to a machine-learning algorithm configured to generate control data to achieve a defined quality goal. A controller then independently controls the thermal zones to locally heat, cool or maintain the temperature of the component element according to the control data to advance the component element or composite structure to the defined quality goal. This may be performed over a plurality of instances during which the machine-learning algorithm learns to increase advancement of the component element or composite structure to the defined quality goal.

A controller teaches a teaching point of a slave axis corresponding to a master axis so as to perform a synchronous operation. The controller calculates a teaching range based on one moving speed pattern selected from a plurality of moving speed patterns of the master axis which are preliminarily registered, a preliminarily-set allowable speed in an operation of the slave axis, and a calculated teaching range, in which teaching can be performed, of a following teaching point, so as to display the teaching range on a display device.