Methods and systems for using a teleoperation system to train a robot to perform tasks using machine learning are described herein. A teleoperation system may be used to record actions of a robot as used by a human teleoperator. The teleoperation system may provide a teleoperator insight into the state of the robot and may provide feedback to the teleoperator allowing the teleoperator to feel what the robot is feeling. For example, sensor information from the robot may be sent to the teleoperation system and output to the teleoperator in various forms including vibrations, video, visual cues, or sound. The teleoperation system may output visual guides to the teleoperator so that the teleoperator may know how to control the robot to complete a task in a desired manner.

Robotics control system (10) and method for training said robotics control system are provided. Disclosed embodiments make a gracefully blended utilization of Reinforcement Learning (RL) with conventional control by way of a dynamically adaptive interaction between respective control signals (20, 24) generated by a conventional feedback controller (18) and an RL controller (22). Additionally, disclosed embodiments make use of an iterative approach for training a control policy by effective use of virtual sensor and actuator data (60) interleaved with real-world sensor and actuator data (54). This is effective to reducing a training sample size to fulfill a blended control policy for the conventional feedback controller and the reinforcement learning controller. Disclosed embodiments may be used in a variety of industrial automation applications.

Disclosed herein are an artificial intelligence-based apparatus and method for providing wake-up and bed time information. The artificial intelligence-based apparatus for providing wake-up and bed time information includes a communication unit configured to receive usage information for an electronic device used by a user from the electronic device; a memory configured to store the usage information; and a processor configured to load the usage information from the memory, analyze usage time of the electronic device, extract a life pattern of the user, and predict an average wake-up or bed time of the user based on the life pattern. According to the embodiment of the present invention, it is possible to provide services for tasks which the user needs to do at the wake-up or bed time and provide convenience to the user.

An artificial intelligence (AI) home monitoring device including a camera configured to monitor a home environment including a home appliance controlled by the AI home monitoring device; and a processor configured to in response to the monitored home environment including a detection of a first user intending to use the home appliance, check an authority of the first user based on mapping data mapping the home appliance, the first user and a predetermined condition associated with using the home appliance, and compare the predetermined condition with a condition of a current state of the authority of the first user based on the mapping data, in response to the authority of the user matching a preset authority authorizing the first user to use the home appliance, and the predetermined condition associated with using the home appliance matching the condition of the current state of the authority of the user, control the home appliance to allow the first user to use the home appliance, and in response to the authority of the user not matching the preset authority authorizing the first user to use the home appliance, control the home appliance to prevent the first user from using the home appliance.

The present disclosure relates to a robot master control system. The robot master control system includes: a master controller, configured to control at least one dual-robot control system, where each of the least one dual-robot control system includes a first robot, a second robot, and a sub-controller controlling the first robot and the second robot, and the sub-controller is controlled by the master controller. In the present disclosure, multiple robots may be coordinated and comprehensively controlled to grab and move objects. Compared with a single robot, the efficiency of the multiple robots operation is greatly improved. In addition, each dual-robot control system may be individually configured, thereby improving the work efficiency of coordinated work of dual-robot control systems.

Systems, methods, and related technologies are disclosed for multi-device robot control. In one implementation, input(s) are received and provided to a personal assistant or another application or service. In response, command(s) directed to an external device are received, e.g., from the personal assistant. Based on the command(s), a robot is maneuvered in relation to a location associated with the external device. Transmission of instruction(s) from the robot to the external device is initiated.

A platform for providing employment assistance services to enterprises and candidates is disclosed. For example, the platform trains, based on personal attributes of employees of multiple enterprise and work culture attributes associated with each employer of the multiple enterprises, a machine learning model that defines associations between the personal attributes and the work culture attributes. Further, the platform extracts information associated with a person from one or more sources on the Web where the person is represented, generates, based on the information associated with the person, a user profile associating personal attributes with the person, applies the user profile to the machine learning model; and receives an indication, from the machine learning model, of an employer profile that is compatible with the user profile, the employer profile including work culture attributes associated with an employer.

A platform for providing employment assistance services to enterprises and candidates is disclosed. For example, the platform trains, based on personal attributes of employees of multiple enterprise and work culture attributes associated with each employer of the multiple enterprises, a machine learning model that defines associations between the personal attributes and the work culture attributes. Further, the platform extracts information associated with a person from one or more sources on the Web where the person is represented, generates, based on the information associated with the person, a user profile associating personal attributes with the person, applies the user profile to the machine learning model; and receives an indication, from the machine learning model, of an employer profile that is compatible with the user profile, the employer profile including work culture attributes associated with an employer.

An object of the present invention is to achieve prediction of an indication, drug repositioning and/or drug repurposing for a drug with no known adverse events and/or side effects based on adverse events and/or side effects.

The problem is solved by a method for predicting an indication for a drug of interest or its equivalent substance, including inputting estimated adverse event-related information and/or estimated side effect-related information estimated from a set of data indicating the behavior of a biomarker in one or more organs collected from non-human animals to which the drug of interest or its equivalent substance has been administered as a test substance into an artificial intelligence model for prediction as test data to predict an indication for the drug of interest or its equivalent substance.

An electronic apparatus is provided. The electronic apparatus includes a camera, a processor and a memory configured to store at least one instruction executable by the processor where and the processor is configured to input audio data to an artificial intelligence model corresponding to user information, and obtain output audio data from the artificial intelligence model, and the artificial intelligence model is a model learned based on first learning audio data obtained by recording a sound source with a first recording device, second learning audio data obtained by recording the sound source with a second recording device, and information on a recording device for obtaining the second learning audio data, and the second learning audio data is binaural audio data.