Various embodiments of the present technology provide an integrated platform that provides recommendation tools for various phases of an industrial automation project lifecycle. In accordance with various embodiments, the integrated platform provides a master hub connecting data from multiple parties (e.g., distributors, end users, etc.). The integrated platform can include a security layer identifying which data an individual can access (e.g., based on a current role), ingest that data along with current activity from the user to provide customized recommendations. Various embodiments can use a common, cross-platform data file that links data and activity to efficiently provide needed information to a user. In some embodiments, the data (e.g., historical sales data, maintenance records, etc.) can be used to create installed base evaluations which can be used to create customized spare part inventory recommendations.

A workpiece picking device includes a sensor measuring a plurality of workpieces randomly piled in a three-dimensional space; a robot folding the workpieces; a hand mounted to the robot and hold the workpieces; a holding position posture calculation unit calculating holding position posture data of a position and a posture to hold the workpieces by the robot based on an output of the sensor; a loading state improvement operation generation unit generating loading state improvement operation data of improving a loading state of the workpieces by the robot based on an output of the sensor; and a robot control unit controlling the robot and the hand. The robot control unit controls the robot and the hand based on an output of the holding position posture calculation unit and the loading state improvement operation generation unit to pick the workpieces or perform a loading state improvement operation.

A system for determining a processing procedure including a plurality of processes for controlling an object, the system includes a learning unit for performing a learning process for determining a processing condition of each of a plurality of processes, and the learning unit acquires a physical quantity correlating with a state of the object on which a process has been performed under a predetermined processing condition, from a device for controlling the object on the basis of the processing procedure, calculates a pseudo state corresponding to the state of the object on the basis of the physical quantity, performs a learning process using a value function, and determines a processing condition of each of the plurality of processes to achieve a target state of the object.

A method and an apparatus for exclusive reinforcement learning are provided, comprising: collecting information of states of an environment through the communication interface and performing a statistical analysis on the states using the collected information; determining a first state value of a first state among the states in a training phase and a second state value of a second state among the states in an inference phase based on analysis results of the statistical analysis; performing reinforcement learning by using one reinforcement learning unit of a plurality of reinforcement learning unit which performs reinforcement learnings from different perspectives according to the first state value; and selecting one of actions determined by the plurality of reinforcement learning unit based on the second state value and applying selected action to the environment.

Embodiments described herein provide a system for facilitating comprehensive control data for a device. During operation, the system determines one or more properties of the device that can be applied to empirical data of the device. The empirical data can be obtained based on experiments performed on the device. The system applies the one or more properties to the empirical data to obtain derived data and learns an efficient policy for the device based on both empirical and derived data. The efficient policy indicates one or more operations of the device that can reach a target state from an initial state of the device. The system then determines an operation for the device based on the efficient policy.

A computer-implemented method executed by a robotic system for performing a positional search process in an assembly task is presented. The method includes decomposing, by the robotic system, a perturbation motion into a plurality of actions, the perturbation motion being a motion for an assembly position searched by the robotic system, each action of the plurality of actions related to a specific direction. The method further includes performing reinforcement learning by selecting an action among decomposed actions and assembly movement actions at each step of the positional search process based on corresponding force-torque data received from at least one sensor associated with the robotic system. The method also includes outputting a best action at each step for completion of the assembly task as a result of the reinforcement learning.

A system for manufacturing a semiconductor device includes a main system controller, a sub-system controller, and a process module. The main system controller provides a process recipe for manufacturing the semiconductor device and step identification information indicating one of a plurality of operations in the process recipe. The sub-system controller sets a process control variable based on the process recipe and the step identification information received from the main system controller. The process module perform the process recipe based on an input value determined by the process control variable

A work center in a manufacturing setup includes a machine learning model that uses a decision tree to facilitate the work of a supervisor on the production line to choose a machine to perform a particular operation on a particular part. The decision tree outputs a ranking of machines indicating the suitability of the ranked machines for performing the particular operation on the particular part.

A method for minimizing the rate of collision of a mobile robotic device. A mobile robotic device selects controls comprised of sets of actions to navigate through a workspace. Controls resulting in non-collisions cause the system to earn a positive reward. Controls resulting in collisions cause the system to earn a negative reward. Cumulative rewards over the course of a work session are compared to the cumulative rewards of other work sessions. A policy is defined based on outcomes of prior work sessions to minimize the expected number of collisions.

A wire electric discharge machine according to the present invention includes a machine learning device which performs machine learning for adjustment of a machining condition of the wire electric discharge machine, the machine learning device includes a state observation unit which acquires data related to a machining state of a workpiece, a reward calculation unit which calculates a reward based on data related to a machining state, a machining condition adjustment learning unit which determines an adjustment amount of a machining condition based on a machine learning result and data related to a machining state, and a machining condition adjustment unit which adjusts a machining condition based on the determined adjustment amount of a machining condition, and the machining condition adjustment learning unit performs machine learning for adjustment of a machining condition based on the determined adjustment amount of a machining condition, data related to a machining state and acquired by the state observation unit, and a reward which is calculated by the reward calculation unit.