Hitachi, Ltd.

Yuya Okadome

Toshiko Aizono

Wenpeng Wei

Ryo Sakai

A robot control system includes a state candidate generation unit that generates a state candidate that is a state transition destination of a robot at next time, a control amount estimation unit that estimates a control amount for transitioning to the state candidate, a state candidate evaluation unit that calculates a distance between the target state of the robot and the state candidate, calculates a coincidence degree between (i) a state at next time estimated from a state at current time of the robot and the control amount and (ii) the state candidate, and sets a sum of the distance and the coincidence degree to be an evaluation value, and a selection unit that selects a state candidate with a minimum evaluation value from state candidates and generate a motion corresponding to the selected state candidate.

Robot control system and robot control method

An error in a demand prediction of items is reduced. A data processing apparatus for processing data on a demand for multiple items includes a model learning unit that generates a prediction model for predicting the demand for the multiple items, and a demand prediction unit that predicts the demand for the multiple items by using the prediction model. The model learning unit inputs actual data of the demand for each item to a first neural network of each item, extracts a feature quantity of each item, and combines the feature quantity of each item to generate a second neural network that is a prediction model.

Data processing apparatus and data processing method

In view of the relevant background art, the present invention is to provide a system or apparatus which enables data in use for learning to be sorted out, thereby reducing the amount of data to be handled. Presenting one example of the control system according to the present invention, the control system including a first processing device to generate a learnt model; and a second processing device to operate employing the generated learnt model and provided with input means, wherein the second processing device transmits information input from the input means to the first processing device; and where the input information as transmitted is information belonging to an area different from an area designated with the learnt model, the first processing device generates an updated learnt model based on the information belonging to the different area as received and the learnt model.

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Tokyo, Japan

Yuya Okadome