Preferred Networks, Inc.

Fanuc Corporation

Keigo Kawaai

Daisuke Okanohara

Eiichi Matsumoto

Ryosuke Okuta

Hiroshi Nakagawa

Shougo Inagaki

Kazutaka Nakayama

Hidetoshi Kumiya

Takumi Oyama

Shun Suyama

Takashi Yamazaki

Taketsugu Tsuda

Daisuke Tanaka

Shohei Hido

Nobuyuki Kubota

An anomality prediction system, which predicts an anomality of a machine, includes: one or more memories; and one or more processors configured to: obtain a state variable including at least one of output data from at least one sensor that detects a state of at least one of the machine or a surrounding environment, internal data of control software controlling the machine, or computational data obtained based on at least one of the output data or the internal data; generate, by inputting the obtained state variable into a machine learning model, a degree of anomality of the machine based on output from the machine learning model; and notify information based on the generated degree of anomality, wherein the notified information includes at least one of the generated degree of anomality at one or more time points, or one or more levels of anomality based on the generated degree of anomality.

Fault prediction method and fault prediction system for predecting a fault of a machine

A machine learning device for a robot that allows a human and the robot to work cooperatively, the machine learning device including a state observation unit that observes a state variable representing a state of the robot during a period in that the human and the robot work cooperatively; a determination data obtaining unit that obtains determination data for at least one of a level of burden on the human and a working efficiency; and a learning unit that learns a training data set for setting an action of the robot, based on the state variable and the determination data.

Machine learning device, robot controller, robot system, and machine learning method for learning action pattern of human

A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of objects placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each object, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the object by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the object, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Machine learning device, robot system, and machine learning method for learning object picking operation

[Problem] To provide a learning device for performing more efficient machine learning. [Solution] A learning device unit according to one embodiment comprises at least one learning device and a connection device for connecting an intermediate learning device having an internal state shared by another learning device unit to the at least one learning device.

Learning device unit

A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Server device, learned model providing program, learned model providing method, and learned model providing system

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

Machine learning Method and machine learning device for learning fault conditions, and fault prediction device and fault prediction system including the machine learning device

A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Machine learning device, robot system, and machine learning method for learning workpiece picking operation

Machine learning method and machine learning device for learning fault conditions, and fault prediction device and fault prediction system including the machine learning device

Growing community of inventors

Tokyo, Japan

Keigo Kawaai