Omron Corporation

Ryo Yonetani

Mai Kurose

Yoshihisa Ijiri

Masaki Suwa

Atsushi Hashimoto

Hiroyuki Miyaura

Tatsunori Taniai

Yoshiaki Miyata

Yamato Okamoto

Mohammadamin Barekatain

Masahiro Nakata

A robot control model learning device () performs, by using state information indicating the state of a robot which autonomously travels to a destination in a dynamic environment as an input, reinforcement learning to obtain a robot control model for selecting and outputting a behavior in accordance with the state of the robot from among a plurality of behaviors including an intervention behavior for intervening in the environment, while using the number of times the intervention behavior has been performed as a minus reward.

Robot control model learning method for reducing frequency of robot intervention behavior

A model generation apparatus according to one or more embodiments executes operations, with respect to each of learning data sets. The operations includes training a second estimator so that an estimation result obtained from a second estimator conforms to second correct answer data; training a coder so that an estimation result obtained from the second estimator does not conform to the second correct answer data; and training the coder and the first estimator so that an estimation result obtained from a first estimator conforms to first correct answer data. The model generation apparatus executes operation of the training the second estimator and the training the coder alternately and repeatedly.

Model generation apparatus, estimation apparatus, model generation method, and computer-readable storage medium storing a model generation program including training a model by converting training data to deliberately reduce estimation performance for improved defect detection and feature identification

A model generation apparatus trains, through machine learning, a neural network module that includes an extraction operation to extract an element satisfying a predetermined condition from a set of targets. In the machine learning, the model generation apparatus performs the extraction operation in a phase of forward propagation with the neural network module, and replaces, in a phase of backpropagation, the extraction operation with a differentiable alternative operation and differentiates the alternative operation to compute an approximate gradient corresponding to differentiation for the extraction operation.

Model generation apparatus, regression apparatus, model generation method, and computer-readable storage medium storing a model generation program

An inference apparatus provides target data to multiple inference models to cause the inference models each derived from local learning data obtained in a different environment to perform predetermined inference to obtain an inference result from each of the inference models. The inference apparatus determines the value of each combining parameter using environment data, weights the inference result from each of the inference models using the determined value of each combining parameter, and combines the weighted inference result from each inference model together to generate an inference result in a target environment.

Inference apparatus, inference method, and computer-readable storage medium storing an inference program

A method for use with a computing device is provided. The method may include inputting an input data set into a first private artificial intelligence model generated using a first private data set and a second private artificial intelligence model generated using a second private data set. The method may further include receiving a first result data set from the first private artificial intelligence model and receiving a second result data set from the second private artificial intelligence model. The method may further include training an adaptive co-distillation model with the input data set and the first result data set. The method may further include training the adaptive co-distillation model with the input data set and the second result data set. The adaptive co-distillation model may not be trained on the first private data set or the second private data set.

Adaptive co-distillation model

A method for modeling crowd movement includes obtaining a temporal sequence of images of a physical venue and, for each of the images, subdividing the respective image into a respective set of logical pixels according to a predetermined mapping. For each logical pixel of each image, the method computes a respective crowd density representing a respective number of mobile objects per unit of area in the physical venue at the logical pixel, thereby forming a temporal sequence of crowd density maps that corresponds to the temporal sequence of images. The method then uses successive pairs of crowd density maps to train a model on spatiotemporal changes in crowd density at the physical venue. A method of predicting future crowd density maps at physical venues using a current image of the physical venue and the trained model is also disclosed.

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Tokyo, Japan

Ryo Yonetani