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 machine learning device, which learns an operation program of a robot, includes a state observation unit which observes as a state variable at least one of a shaking of an arm of the robot and a length of an operation trajectory of the arm of the robot; a determination data obtaining unit which obtains as determination data a cycle time in which the robot performs processing; and a learning unit which learns the operation program of the robot based on an output of the state observation unit and an output of the determination data obtaining unit.

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.

There is provided a method for learning a bipedal robot control policy, the method includes (i) learning, by a processing circuit, an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function; and (ii) determining a control policy of the bipedal robot in a simulator, using the action-related corrective policy.

A system and method for mitigating data drift in an industrial plant includes monitoring, by a processor, one or more process parameters associated with an industrial plant; detecting, by the processor, a drift in one or more process parameters based on a deviation from one or more predefined process parameters; determining, by the processor, one or more drift context and process context based on drift and one or more process parameters; determining, by the processor, sampling strategy from plurality of sampling strategies based on one or more drift and process context for sampling one or more process parameters using first Artificial Intelligence (AI) model; and training, by the processor, a second AI model based on sampling strategy for mitigating data drift.

Various embodiments include methods for controlling an industrial device. Some embodiments include: obtaining a state input characterizing a current state of the industrial device; processing the state input to generate an action output characterizing an expected action to be performed by the industrial device for the current state, based on a machine learning model trained based on states of the industrial device, actions each performed for each state of the industrial device and results each obtained by performing each action; and generating a control signal for the industrial device based on the action output.