A computer implemented method for automatically generating a behavior tree program for controlling a machine includes the steps of: transmitting a sequence of machine commands input by a user from a user interface to a controller, receiving supervision data in the user interface from the controller while the machine commands are executed in the controller controlling the machine, observing and copying the machine commands and supervision data transmitted between the controller and the user interface, storing the machine commands and the supervision data in a logging unit, generating a behavior tree program derived from the stored machine commands and the supervision data by statistical inference, and sending the generated behavior tree program to the controller unit to control the machine.

Systems and methods for automatically assigning labels to one or more types of non-conforming behavior of execution of a process are provided. An aligned process defining non-conforming behavior of execution of a process is received. One or more types of the non-conforming behavior of the execution of the process is identified from the aligned process. Labels identifying the one or more types are assigned to the non-conforming behavior. The labels assigned to the non-conforming behavior are output.

A method is provided for causing one or more robots to execute a mission. The method includes determining a behavior tree in which the mission is modeled, and causing the one or more robots to execute the mission using the behavior tree and a leaf node library. The behavior tree is expressed as a directed tree of nodes including a switch node, a trigger node representing a selected task, and action nodes representing others of the tasks. The switch node is connected to the trigger node and the action nodes in a parent-child relationship in which the trigger node and the action nodes are children of the switch node. The trigger node is a first of the children that, when ticked by the switch node, returns an identifier of one of the action nodes to trigger the switch node to next tick the one of the action nodes.

A computer implemented method for automatically generating a behavior tree program for controlling a machine includes the steps of: transmitting a sequence of machine commands input by a user from a user interface to a controller, receiving supervision data in the user interface from the controller while the machine commands are executed in the controller controlling the machine, observing and copying the machine commands and supervision data transmitted between the controller and the user interface, storing the machine commands and the supervision data in a logging unit, generating a behavior tree program derived from the stored machine commands and the supervision data by statistical inference, and sending the generated behavior tree program to the controller unit to control the machine.

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.

A method includes acquiring process data collected in an industrial process control and automation system. The method also includes reducing a dimension space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data. The method further includes determining a change in a process based on a change in the process data in the reduced dimension space. The method also includes outputting a result based on the determined change in the process.

Methods and systems for enabling human-machine collaborations include a generalizable framework that supports dynamic adaptation and reuse of robotic capability representations and human-machine collaborative behaviors. Specifically, a method of feedback-enabled user-robot collaboration includes obtaining a robot capability that models a robot's functionality for performing task actions, specializing the robot capability with an information kernel that encapsulates task-related parameters associated with the task actions, and providing an instance of the specialized robot capability as a robot capability element that controls the robot's functionality based on the task-related parameters. The method also includes obtaining, based on the robot capability element's user interaction requirements, user interaction capability elements, via which the robot capability element receives user input and provides user feedback, controlling, based on the task-related parameters, the robot's functionality to perform the task actions in collaboration with the user input; and providing the user feedback including task-related information generated by the robot capability element in association with the task actions.

A method includes acquiring process data collected in an industrial process control and automation system. The method also includes reducing a dimension space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data. The method further includes determining a change in a process based on a change in the process data in the reduced dimension space. The method also includes outputting a result based on the determined change in the process.

The present invention provides a behavior identification device that can identify various behaviors without specifically defining a component constituting a behavior in advance. The behavior identification device comprising: a sensor-value obtaining unit (10) that obtains the sensor value and calculates a sensor value distribution that is a distribution of the sensor value measured within a predetermined time; a component database (42) that stores therein a set of basic distributions that are basic components constituting the sensor value distribution; a ratio calculating unit (21) that calculates a first component ratio that is a ratio of each of the basic distributions included in the sensor value distribution; a component ratio database (43) that stores therein a second component ratio that is the ratio determined in association with a behavior to be identified; and an identification unit (22) that compares the first component ratio to the second component ratio to identify the behavior, wherein the basic distribution is calculated as a sensor value distribution that is a base when each sensor value distribution is assumed to be a vector based on a set of the sensor value distributions obtained in advance for each of a plurality of types of the behavior.

A method of controlling a robot includes: generating a plurality of behavior objects corresponding to a plurality of behaviors to be performed by the robot; calculating an expected time expected to be taken by the robot to perform each of the behaviors; mapping each of the behavior object to the expected time; adding the behavior objects to a behavior flow list; and displaying the behavior flow list.