In a controller and a machine learning device capable of suppressing an influence of an abnormal value based on noise, etc., the machine learning device included in the controller includes a state observation unit for acquiring input data including at least one of internal data and external data of the manufacturing machine controlled by the controller, an input safety circuit for detecting an abnormality in the input data and outputting safe input data, a machine learning unit for executing learning of a learning model and inference using the learning model based on the safe input data and outputting inference data as an inference result, an output safety circuit for detecting an abnormality in the inference data and outputting safe inference data, and an output unit for outputting output data based on the safe inference data.

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.

In a controller and a machine learning device capable of suppressing an influence of an abnormal value based on noise, etc., the machine learning device included in the controller includes a state observation unit for acquiring input data including at least one of internal data and external data of the manufacturing machine controlled by the controller, an input safety circuit for detecting an abnormality in the input data and outputting safe input data, a machine learning unit for executing learning of a learning model and inference using the learning model based on the safe input data and outputting inference data as an inference result, an output safety circuit for detecting an abnormality in the inference data and outputting safe inference data, and an output unit for outputting output data based on the safe inference data.

A program creating device according to the present invention includes a definition information acquiring unit to acquire database definition information that is information indicating a configuration of a database in a programmable logic controller; and a program component generating unit to generate program components to be used in creating a program for operating the database on the basis of the database definition information.

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.

There is provided a method and system for generating a program. The method includes detecting a number of steps for performing a task on a computing device and detecting an example relating to each of the steps, wherein the example includes input data and corresponding output data relating to the step. The method also includes, for each example, determining a rule that transforms the input data to the corresponding output data based on cues including textual features within the input data and the corresponding output data. The method further includes generating a program for performing the task based on the rules.

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.