A system for controlling interactions between a plurality of real and virtual robots, includes one or more real robots present in the real environment, one or more virtual robots present in a virtual environment corresponding to the real environment, and a processing device operable to control interactions between one or more of the real robots and one or more of the virtual robots, where the interactions between the real and virtual robots are dependent upon at least the positions of the one or more real robots in the real environment and the positions of the one or more virtual robots in the virtual environment.

A learning SONAR system and method including receiving, at an input, mission parameters including one or more of mission accuracy, mission covertness, learning rate, and training matrix dependency; transmitting pulsed signals; receiving return pulsed signals, for instance, using a tunable acoustic receiver having controllable receiver elements; and determining a number of the controllable receiver elements to generate estimates of altitude and 3D velocity based on a combination of transmit power, signal-to-noise ratio, and altitude range using an adaptive spatial sampler of a learning controller.

A computer-implemented method includes establishing, by a computer device, an activity to be performed by a robot; determining, by the computer device, a required knowledge that is required for the robot to perform the activity; comparing, by the computer device, the required knowledge to a current knowledge of the robot to establish an additional learning that is needed for the robot to perform the activity; requesting, by the computer device, the additional learning; directing, by the computer device, retrieval of the additional learning to the robot if the additional learning is available for retrieval; and requesting, by the computer device, that the additional learning be created if the additional learning is not available for retrieval.

Examples of a sentence phrasing system are provided. The system may obtain a user question from a user. The system may obtain question entailment data from a plurality of data sources. The system may implement an artificial intelligence component to identify a word index from the question entailment data and to identify a question premise from the user question. The system may implement a first cognitive learning operation to determine an answer premise corresponding to the question premise comprising a second-word data set. The system may determine a subject component corresponding to the question premise. The system may generate an object component and a predicate component from the second-word data set corresponding to the subject component. The system may generate an integrated answer relevant for resolving the user question and comprising the subject component, the object component, and the predicate component concatenated to form an answer sentence.

A system includes a neural network system implemented by one or more computers. The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation. The neural network system includes: (i) a sequence of deep neural networks (DNNs), in which the sequence of DNNs includes a simulation-trained DNN that has been trained on interactions of a simulated version of the robotic agent with a simulated version of the real-world environment to perform a simulated version of the robotic task, and (ii) a first robot-trained DNN that is configured to receive the observation and to process the observation to generate the policy output.

Embodiments of architecture, systems, and methods for modeling dynamics between behavior and emotional states in an artificial nervous system are described herein. A computer implemented emotion system of an artificial nervous system for animating a virtual object, digital entity, or robot, is provided, comprising: a plurality of states, each state of the plurality of states representing an emotional state (ES) of the artificial nervous system; a module for processing a plurality of inputs, the processed plurality of inputs applied to the plurality of states. Other embodiments may be described and claimed.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a representation of an image captured by an onboard camera of a drone and providing the representation of the image to a drone image processing neural network having a brain emulation sub-network with an architecture that is specified by synaptic connectivity between neurons in a brain of a biological organism, including instantiating a respective artificial neuron corresponding to each biological neuron of multiple biological neurons, and instantiating a respective connection between each pair of artificial neurons that correspond to a pair of biological neurons that are connected by a synaptic connection, and processing the representation of the image using the drone image processing neural network having the brain emulation sub-network to generate a network output that defines a prediction characterizing the image captured by the onboard camera of the drone.

An abnormality detection device 10, which detects an abnormality of a data series to be detected that has regularity in a sequence of data forming the data series, is provided with: a determination unit 11 which refers to a data series of a normal model composed of a prescribed permutation as a data series that indicates a state in which a system to be detected is normal, and which, every time one piece of data is input, in light of a permutation indicated by a pair of the one piece of input data and another piece of data input immediately before the one piece of data is input, determines that the data series to be detected is locally abnormal when the permutation is not included in the normal model, and determines that the data series to be detected is locally normal when the permutation is included in the normal model.

A communication system according to the present disclosure includes a camera configured to be able to photograph a user who is a communication partner and a microphone configured to be able to form a beam-forming in a specific direction. The control unit identifies a position of the mouth of a user using an image of the user taken by the camera and controls a position of a head part so that the identified position of the mouth of the user is included in a region of the beam-forming.

A moving target following method, which is executed by one or more processors of a robot that includes a camera and a sensor electrically coupled to the one or more processors, includes: performing a body detection to a body of a target based on images acquired by the camera to obtain a body detection result; performing a leg detection to legs of the target based on data acquired by die sensor to obtain a leg detection result; and fusing the body detection result and the leg detection result to obtain a fusion result, and controlling the robot to follow the target based on the fusion result.