Abstract: An information processing device includes: a foreground state estimating unit configured to estimate a foreground state of an image using an actual image which is an image to be actually observed; and a visible model updating unit configured to update a background visible model which is visibility of the background of an image and a foreground visible model which is visibility of the foreground using an estimation result of the foreground state.

Abstract: An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action.

Abstract: There is provided an information processing apparatus including an information amount gain calculation unit that, on the assumption that a factor that could cause a state transition in a state transition model occurs and the state transition takes place as a result of an occurrence of the factor, determines an information amount gain, which is an information amount obtained by causing the factor to occur regarding a causal relationship between the factor and the state transition and observing a result thereof, an evaluation value calculation unit that determines an evaluation value to evaluate effectiveness of causing each of the factors to occur based on the information amount gain to acquire the causal relationship between the factor and the state transition, and a decision unit that decides the factor to be occurred based on the evaluation value.

Abstract: Provided is an information processing apparatus including a learning part performing learning of a model of an environment in which an agent performs action, using an observed value observed in the agent when the agent capable of action performs action, an action determining part determining action to be performed by the agent, based on the model, and a user instruction output part outputting instruction information representing an instruction from a user according to the instruction from the user, wherein the action determining part determines the action performed by the agent according to the instruction information when there is an instruction from the user.

Abstract: In some embodiments, a system comprising a first and a second computing device is disclosed. The first computing device, comprising at least one processor, is configured to obtain, from a second computing device, data related to power consumption by at least a first electrical device located in a first region of a plurality of regions, and classify the first region, based at least in part on the obtained data, into a first group of regions of a plurality of groups of regions. The second computing device, comprising at least another processor, is configured to transmit, to the first computing device, the data related to power consumption by at least the first electrical device, and receive, from the first computing device information related to power consumption by a second electrical device located in a second region, wherein the second region is in the first group of regions.

Abstract: There is provided an information processing apparatus including: an appliance power consumption estimating unit estimating power consumption of each of a plurality of appliances disposed inside a large region that is divided into a plurality of regions; an appliance presence probability estimating unit estimating appliance presence probabilities that are probabilities that the respective appliances are present in the respective regions; a responsible share deciding unit deciding responsible shares that are proportions for respective people when power consumption in each region is shared among people who may be present in the large region; and a power consumption allocating unit calculating an allocated amount of power consumption of each person based on the power consumption of each of the plurality of appliances, the appliance presence probabilities, and the responsible shares.

Abstract: Provided is a robot device including an image input unit for inputting an image of surroundings, a target object detection unit for detecting an object from the input image, an object position detection unit for detecting a position of the object, an environment information acquisition unit for acquiring surrounding environment information of the position of the object, an optimum posture acquisition unit for acquiring an optimum posture corresponding to the surrounding environment information for the object, an object posture detection unit for detecting a current posture of the object from the input image, an object posture comparison unit for comparing the current posture of the object to the optimum posture of the object, and an object posture correction unit for correcting the posture of the object when the object posture comparison unit determines that there is a predetermined difference or more between the current posture and the optimum posture.

Abstract: In a plane detection apparatus, a plane detection unit (3) includes a line fitting block (4) to select a group of distance data points being in one plane from distance data forming an image and extract lines from the distance data point group, and a region growing block (5) to detect one or more planar regions existing in the image from a group of all lines included in the image and extracted by the line fitting block (4). The line fitting block (4) first draws a line D1 connecting end points of the distance data point group, searches a point of interest brk whose distance to the line L1 is largest, segments the data point group by the point of interest brk when the distance is larger than a predetermined threshold, and determines a line L2 by the least-squares method when the distance is smaller than the predetermined threshold.

Abstract: An information processing apparatus includes: model learning means for self-organizing, on the basis of a state transition model having a state and state transition to be learned by using time series data as data in time series, an internal state from an observation signal obtained by a sensor; and controller learning means for performing learning for allocating a controller, which outputs an action, to each of transitions of a state or each of transition destination states in the state transition model indicating the internal state self-organized by the model learning means.

Abstract: An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

Abstract: A data processing device includes a state value calculation unit which calculates a state value of which the value increases as much as a state with a high transition probability for each state of the state transition model, an action value calculation unit which calculates an action value, of which the value increases as a transition probability increases for each state of the state transition model and each action that the agent can perform, a target state setting unit which sets a state with great unevenness in the action value among states of the state transition model to a target state that is the target to reach by action performed by the agent, and an action selection unit which selects an action of the agent so as to move toward the target state.

Abstract: Provided is a torque detection apparatus including a base portion, a drive portion, and a detection portion. The drive portion includes a rotor having a main axis in a direction of a first axis, and a stator configured to rotate the rotor around the main axis. The detection portion includes a strain body and a detection element. The strain body includes a first end portion to be fixed to the base portion and a second end portion to be fixed to the rotor, and is arranged concentrically with the rotor. The detection element is provided to the strain body so as to detect a strain of the strain body around the first axis with respect to the base portion.

Abstract: A data processing device includes a parameter estimation unit and a structure adjustment unit. The structure adjustment unit notes each state of an HMM as a noted state, obtains, for the noted state, a value corresponding to an eigen value difference which is a difference between a partial eigen value sum and a total eigen value sum, as a target degree value indicating a degree for selecting the noted state as a division target or a mergence target, selects a state having the target degree value larger than a division threshold value, as a division target, and selects a state having the target degree value smaller than a mergence threshold value, as a mergence target.

Abstract: There is provided a robot device including an instruction acquisition unit that acquires an order for encouraging a robot device to establish joint attention on a target from a user, a position/posture estimation unit that estimates a position and posture of an optical indication device, which is operated by the user to indicate the target by irradiation of a beam, in response to acquisition of the order, and a target specifying unit that specifies a direction of the target indicated by irradiation of the beam based on an estimation result of the position and posture and specifies the target on an environment map representing a surrounding environment based on a specifying result of the direction.

Abstract: There is provided a gripping judgment apparatus including a plan unit that generates a target orbit for moving a gripping unit in a state in which an object as a gripping target is gripped by the gripping unit, an observation unit that measures movement of the gripping unit driven based on the target orbit, a gripping state judgment unit that judges whether or not an object as a gripping target is grippable based on a target value of the gripping unit derived from the target orbit and an actual measured value measured by the observation unit, and a gripping state change unit that changes a gripping state of an object gripped by the gripping unit based on a judgment result obtained by the gripping state judgment unit.

Abstract: There is provided a robot device including an irradiation unit that irradiates pattern light to an external environment, an imaging unit that acquires an image by imaging the external environment, an external environment recognition unit that recognizes the external environment, an irradiation determining unit that controls the irradiation unit to be turned on when it is determined that irradiation of the pattern light is necessary based on an acquisition status of the image, and a light-off determining unit that controls the irradiation unit to be turned off when it is determined that irradiation of the pattern light is unnecessary or that irradiation of the pattern light is necessary to be forcibly stopped, based on the external environment.

Abstract: A learning apparatus includes a storage unit configured to store a network formed by a plurality of nodes each holding dynamics; a learning unit configured to learn the dynamics of the network in a self-organizing manner on the basis of observed time-series data; a winner-node determiner configured to determine a winner node, the winner node being a node having dynamics that best match the time-series data; and a weight determiner configured to determine learning weights for the dynamics held by the individual nodes according to distances of the individual nodes from the winner node. The learning unit is configured to learn the dynamics of the network in a self-organizing manner by degrees corresponding to the learning weights.

Abstract: An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability bas

Abstract: An object detecting device for detecting an object in a given gradation image. A scaling section generates scaled images by scaling down a gradation image input from an image output section. A scanning section sequentially manipulates the scaled images and cutting out window images from them and a discriminator judges if each window image is an object or not. The discriminator includes a plurality of weak discriminators that are learned in a group by boosting and an adder for making a weighted majority decision from the outputs of the weak discriminators. Each of the weak discriminators outputs an estimate of the likelihood of a window image to be an object or not by using the difference of the luminance values between two pixels. The discriminator suspends the operation of computing estimates for a window image that is judged to be a non-object, using a threshold value that is learned in advance.

Abstract: An object detecting device for detecting an object in a given gradation image. A scaling section generates scaled images by scaling down a gradation image input from an image output section. A scanning section sequentially manipulates the scaled images and cutting out window images from them and a discriminator judges if each window image is an object or not. The discriminator includes a plurality of weak discriminators that are learned in a group by boosting and an adder for making a weighted majority decision from the outputs of the weak discriminators. Each of the weak discriminators outputs an estimate of the likelihood of a window image to be an object or not by using the difference of the luminance values between two pixels. The discriminator suspends the operation of computing estimates for a window image that is judged to be a non-object, using a threshold value that is learned in advance.