Abstract: A method is provided for detecting a higher-level action from one or more trajectories of real states. The trajectories are based on an experts' action demonstration. The method trains predictors to predict future states. Each predictor has a different duration of the higher-level action to be detected. The method predicts, using the predictors, the future states using past ones of the real states in the one or more trajectories as inputs for the predictors. The method determines if a match exists between any of the future states relative to a real future state with a corresponding same duration from the one or more trajectories. The method outputs a pair that includes the matching one of the future states as a prediction input and the real future state with the corresponding same duration from the one or more trajectories as the higher-level action corresponding thereto, responsive to the match existing.

Abstract: A computer-implemented method is provided for multi-source sound localization. The method includes extracting, by a hardware processor, spectral features from respective pluralities of microphones comprised in each of two or more microphone arrays. The method further includes forming, by the hardware processor, respective sets of pairs of the spectral features from the respective pluralities of microphones within each of the two or more microphone arrays by rearranging and duplicating the spectral features from the respective pluralities of microphones included in each of the two or more microphone arrays. The method also includes inputting, by the hardware processor, the respective sets of pairs of the spectral features into a neural network to encode the spectral features into deep features and decode the deep features to output from the neural network at least one location representation of one or more sound sources.

Abstract: A computer-implemented method is provided for modified Lexicographic Reinforcement Learning. The computer implemented method includes obtaining, by a hardware processor, a sequence of tasks. Each of the tasks corresponds to, and has a one-to-one correspondence with, a respective award from among set of rewards. The method further includes performing, by the hardware processor for each of the tasks, reinforcement learning and deep learning for both of (i) one or more policies and (ii) one or more value functions, with a plurality of sets of samples. A plurality of solutions in a form of the one or more policies and the one or more value functions are parametrized by a single neural network with a selector which selects an input of the single neural network from among the plurality of sets of samples.

Abstract: A computer-implemented method executed by a robotic system for performing a positional search process in an assembly task is presented. The method includes decomposing, by the robotic system, a perturbation motion into a plurality of actions, the perturbation motion being a motion for an assembly position searched by the robotic system, each action of the plurality of actions related to a specific direction. The method further includes performing reinforcement learning by selecting an action among decomposed actions and assembly movement actions at each step of the positional search process based on corresponding force-torque data received from at least one sensor associated with the robotic system. The method also includes outputting a best action at each step for completion of the assembly task as a result of the reinforcement learning.

Abstract: A method for learning a classification model by a computer system is disclosed. One or more positive class data and one or more negative class data are prepared. The classification model is trained based on the positive class data to adjust one or more parameters of the classification model so that the positive class data is reconstructed by the classification model. The classification model is trained based on the negative class data to adjust the one or more parameters so that the negative class data is prevented from being reconstructed by the classification model. For the negative class data, changes in the one or more parameters with gradient of an objective function may be calculated using an unsupervised learning algorithm. The one or more parameters may be updated based on the changes in an opposite manner to the training based on the positive class.

Abstract: According to an aspect of the present invention, a computer-implemented method is provided for reinforcement learning. The method includes reading, by a processor device, an action manifold which is described as a n-polytope, at least one physical action limit, and at least one safety constraint. The method further includes updating, by the processor device, the action manifold based on the at least one physical action limit and the at least one safety constraint. The method also includes performing, by the processor device, the reinforcement learning by selecting a constrained action from among a set of constrained actions in the action manifold.

Abstract: A computer-implemented method for inspecting a clearance size between a hole and an object inserted in the hole, includes: controlling a robot arm so that the robot arm performs a predetermined motion to move the object inserted in the hole; monitoring a response to the predetermined motion from the hole and the object; and calculating information on the clearance size between the hole and the object using the response to the predetermined motion.

Abstract: A computer-implemented method is provided for reinforcement learning performed by a processor. The method includes obtaining, from an environment, a given experience that includes an action, a state and a reward. The method further includes storing the given experience in an experience buffer responsive to a value of the reward included in the given experience exceeding a first threshold. The method also includes responsive to obtaining another experience having another reward that less than or equal to the first threshold, searching the experience buffer for a candidate experience with a similar state to the other experience and copying the candidate experience into an event buffer. The method additionally includes during exploration, selecting an action to be taken to the environment from the event buffer with a predetermined probability.

Abstract: A computer-implemented method, computer program product, and system are provided for learning mapping information between different modalities of data. The method includes mapping, by a processor, high-dimensional modalities of data into a low-dimensional manifold to obtain therefor respective low-dimensional embeddings through at least a part of a first network. The method further includes projecting, by the processor, each of the respective low-dimensional embeddings to a common latent space to obtain therefor a respective one of separate latent space distributions in the common latent space through at least a part of a second network. The method also includes optimizing, by the processor, parameters of each of the networks by minimizing a distance between the separate latent space distributions in the common latent space using a variational lower bound. The method additionally includes outputting, by the processor, the parameters as the mapping information.

Abstract: A method of detecting a barcode that in one embodiment includes performing a line segment detection of a barcode image to provide a plurality of line segments, and analyzing the line segments using parallel segment detection to determine a best candidate line segment having a greatest similarity to a remainder of adjacent line segments. The method may further include providing a central bisector of the best candidate line segment, and forming a plurality of parallel lines offset from the central bisector. In a following step, a pixel map is from the central bisector and the plurality of the parallel lines, and an end and a start of the barcode is determined from changes in intensity of the pixels in the pixel map.

Abstract: A computer-implemented method executed by a robotic system for performing a positional search process in an assembly task is presented. The method includes decomposing, by the robotic system, a perturbation motion into a plurality of actions, the perturbation motion being a motion for an assembly position searched by the robotic system, each action of the plurality of actions related to a specific direction. The method further includes performing reinforcement learning by selecting an action among decomposed actions and assembly movement actions at each step of the positional search process based on corresponding force-torque data received from at least one sensor associated with the robotic system. The method also includes outputting a best action at each step for completion of the assembly task as a result of the reinforcement learning.

Abstract: A computer-implemented method is provided for training a classification model. The method includes preparing, by a processor, positive and negative class data. The method further includes iteratively training the classification model, by the processor, using the positive class data and the negative class data such that the positive class data is reconstructed and the negative class data is prevented from being constructed, by the classification model. In response to a selection of a non-integer value as a number of negative learning iterations to be performed to train the classification model, a particular set of the negative class data that is reconstructed best by the classification model from among all of the negative class data is selected to be used for negative learning by the classification model. The training based on the positive class data is performed once before the negative learning iterations and once after each negative learning iteration.

Abstract: A computer-implemented method is provided for training a classification model. The method includes preparing, by a processor, positive and negative class data. The method further includes iteratively training the classification model, by the processor, using the positive class data and the negative class data such that the positive class data is reconstructed and the negative class data is prevented from being constructed, by the classification model. In response to a selection of a non-integer value as a number of negative learning iterations to be performed to train the classification model, a particular set of the negative class data that is reconstructed best by the classification model from among all of the negative class data is selected to be used for negative learning by the classification model. The training based on the positive class data is performed once before the negative learning iterations and once after each negative learning iteration.

Abstract: A computer-implemented method for inspecting a clearance size between a hole and an object inserted in the hole, includes: controlling a robot arm so that the robot arm performs a predetermined motion to move the object inserted in the hole; monitoring a response to the predetermined motion from the hole and the object; and calculating information on the clearance size between the hole and the object using the response to the predetermined motion.

Abstract: A method for barcode detection, barcode detection system and program. In method for detecting a barcode, an information processing apparatus executes the steps of: detecting a plurality of blobs from an image; for each detected blob, obtaining imaginary lines bisecting the detected blob perpendicular at the middle of the detected blob, determining at least one cluster of the imaginary lines in a feature space according to slopes and positions of the imaginary lines and grouping the blobs in one cluster to a rectangular shape corresponding to the barcode.

Abstract: A method of detecting a barcode that in one embodiment includes performing a line segment detection of a barcode image to provide a plurality of line segments, and analyzing the line segments using parallel segment detection to determine a best candidate line segment having a greatest similarity to a remainder of adjacent line segments. The method may further include providing a central bisector of the best candidate line segment, and forming a plurality of parallel lines offset from the central bisector. In a following step, a pixel map is from the central bisector and the plurality of the parallel lines, and an end and a start of the barcode is determined from changes in intensity of the pixels in the pixel map.

Abstract: A method for learning a classification model by a computer system is disclosed. One or more positive class data and one or more negative class data are prepared. The classification model is trained based on the positive class data to adjust one or more parameters of the classification model so that the positive class data is reconstructed by the classification model. The classification model is trained based on the negative class data to adjust the one or more parameters so that the negative class data is prevented from being reconstructed by the classification model. For the negative class data, changes in the one or more parameters with gradient of an objective function may be calculated using an unsupervised learning algorithm. The one or more parameters may be updated based on the changes in an opposite manner to the training based on the positive class.

Abstract: A simulation apparatus that performs parallel execution of multiple logical processes obtained by modeling a plurality of components included in a system to be simulated. The apparatus includes: (i) a condition generating unit configured to generate, on the basis of communication delays between the multiple logical processes, constraint conditions to be satisfied by initial time shifts given to the multiple logical processes and look-ahead times each to be permitted by a message sent from a logical process serving as a communication source to a logical process serving as a communication destination to permit look-ahead; and (ii) a solver unit configured to solve an optimization problem that satisfies the constraint conditions and minimizes overhead in communication of messages between the multiple logical processes, and obtain the initial time shifts of the multiple logical processes and the look-ahead times between the multiple logical processes.

Abstract: A method for barcode detection, barcode detection system and program. In method for detecting a barcode, an information processing apparatus executes the steps of: detecting a plurality of blobs from an image; for each detected blob, obtaining imaginary lines bisecting the detected blob perpendicular at the middle of the detected blob, determining at least one cluster of the imaginary lines in a feature space according to slopes and positions of the imaginary lines and grouping the blobs in one cluster to a rectangular shape corresponding to the barcode.

Abstract: A simulation apparatus that performs parallel execution of multiple logical processes obtained by modeling a plurality of components included in a system to be simulated. The apparatus includes: (i) a condition generating unit configured to generate, on the basis of communication delays between the multiple logical processes, constraint conditions to be satisfied by initial time shifts given to the multiple logical processes and look-ahead times each to be permitted by a message sent from a logical process serving as a communication source to a logical process serving as a communication destination to permit look-ahead; and (ii) a solver unit configured to solve an optimization problem that satisfies the constraint conditions and minimizes overhead in communication of messages between the multiple logical processes, and obtain the initial time shifts of the multiple logical processes and the look-ahead times between the multiple logical processes.