The present disclosure relates to systems and methods for automatically assisting physical activities. The system may include a body condition monitoring unit, and an autonomous companion unit. The body condition monitoring unit may obtain body condition data of a user. The autonomous companion unit may be automatically move alongside the user and guide the user. The autonomous companion unit may include a transporting subunit, a plurality of sensors, and a controller subunit. The transporting subunit may be enable the movement of the autonomous companion unit. The plurality of sensors may obtain surroundings data associated with the autonomous companion unit. The controller subunit may control the transporting subunit to move the autonomous companion unit according to a target movement plan. The target movement plan may include a target route and a target speed profile, which are based on a preliminary movement plan, the surroundings data, and the body condition data.

An identification device has a processor configured to carry out plural identification processing by which an individual is identified based on plural acquired data different from each other indicating the individual and, when the identification of the individual by one or more identification processing of the plural identification processing fails and the identification of the individual by one or more other identification processing of the plural identification processing succeeds, learn the at least one identification processing by which the identification of the individual fails.

According to an embodiment, a learning device includes one or more hardware processors configured to: acquire a current state of a device; learn a reinforcement learning model, and determine a first action of the device on the basis of the current state and the reinforcement learning model; determine a second action of the device on the basis of the current state and a first rule; and select one of the first action and the second action as a third action to be output to the device according to a progress of learning of the reinforcement learning model.

An artificial intelligence apparatus for generating training data includes a memory configured to store a target artificial intelligence model, and a processor configured to receive sensor data, determine whether the received sensor data is irrelevant to a learning of the target artificial intelligence model, determine whether the received sensor data is useful for the learning if the received sensor data is determined to be relevant to the learning, extract a label from the received sensor data by using a label extractor if the received sensor data is determined to be useful for the learning, determine a confidence level of the extracted label, and generate training data including the received sensor data and the extracted label if the determined confidence level exceeds a first reference value.

A detection and tracking system and method using a camera unit on a robot, or alternatively a camera mounted inside the pool overlooking the bottom of the pool, for safety monitoring for use in and around water-related environments. The robot is able to propel itself and move throughout the body of water, both on the surface and underwater, and the camera unit functions both on the surface and underwater. The robot optimizes the cleaning cycle of the body of water utilizing deep learning techniques. The robot has localization sensors and software that allow the robot to be aware of the robot's position in the pool. The camera is able to send its video feed live over the internet, the processing is performed in the cloud, and the robot sends and receives data from the cloud. The processing utilizes deep learning algorithms, including artificial neural networks, that perform video analytics.

A voice recognition method of an artificial intelligence robot device is disclosed. The voice recognition method includes collecting a first voice spoken by a user and determining whether a wake-up word of the artificial intelligence robot device is recognized based on the collected first voice; if the wake-up word is not recognized, sensing a location of the user using at least one sensor and determining whether the sensed location of the user is included in a set voice collection range; if the location of the user is included in the voice collection range, learning the first voice and determining a noise state of the first voice based on the learned first voice; collecting a second voice in an opposite direction of the location of the user according to a result of the determined noise state of the first voice; and extracting a feature value of a noise based on the second voice and removing the extracted feature value of the noise from the first voice to obtain the wake-up word. The artificial intelligence robot device may be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

A method for training a central artificial intelligence module (“AI module”) for highly or fully automated operation of a vehicle, the central AI module to translate input signals into output signals, and the translation is carried out using a processing chain that is adaptable by modifying values of internal processing parameters, wherein the training of the central AI module takes place by modifying the internal processing parameters based on further internal processing parameters of further AI modules, the further AI modules being in a plurality of vehicles and translating input signals into output signals in each case, and the translations taking place using processing chains that are able to be adapted by modifying values of further internal processing parameters, the further AI modules having been trained using input signals that are based on environment data acquired with using environment sensor systems installed in the vehicles.

A method for training a central artificial intelligence module (“AI module”) for highly or fully automated operation of a vehicle, the central AI module to translate input signals into output signals, and the translation is carried out using a processing chain that is adaptable by modifying values of internal processing parameters, wherein the training of the central AI module takes place by modifying the internal processing parameters based on further internal processing parameters of further AI modules, the further AI modules being in a plurality of vehicles and translating input signals into output signals in each case, and the translations taking place using processing chains that are able to be adapted by modifying values of further internal processing parameters, the further AI modules having been trained using input signals that are based on environment data acquired with using environment sensor systems installed in the vehicles.

A system includes a memory module configured to store image data captured by a camera and an electronic controller communicatively coupled to the memory module. The electronic controller is configured to receive image data captured by the camera, implement a neural network trained to predict a drivable portion in the image data of an environment. The neural network predicts the drivable portion in the image data of the environment. The electronic controller is configured to implement a support vector machine. The support vector machine determines whether the predicted drivable portion of the environment output by the neural network is classified as drivable based on a hyperplane of the support vector machine and output an indication of the drivable portion of the environment.

Systems and methods for responding to incidents in a building are provided. One method includes performing operations including retrieving data relating to one or more on-premises building devices of the building, determining the incident relating to the one or more on-premises building devices, and determining a plurality of potential responses to the incident. The operations further include analyzing the plurality of potential responses and determining relative risks of the potential responses to the incident and transmitting, to a first on-premises building device of the on-premises building devices, data indicating the incident and at least one of the plurality of potential responses based on the relative risks of the potential responses.