An apparatus control device for controlling an apparatus includes at least one processor configured to determine, as a characteristic of a performance sound around a robot that is the apparatus, constancy of a performance speed of the performance sound or a key of a performance based on the performance sound, and change a pseudo-emotion of the apparatus in accordance with the determined characteristic of the performance sound.

An apparatus control device for controlling an apparatus includes at least one processor configured to determine, as a characteristic of a performance sound around a robot that is the apparatus, constancy of a performance speed of the performance sound or a key of a performance based on the performance sound, and change a pseudo-emotion of the apparatus in accordance with the determined characteristic of the performance sound.

The present invention provides a facial acupoint locating method, an acupuncture method, an acupuncture robot, and a storage medium. The facial acupoint locating method includes: collecting an RGB image and a depth image of a face by using a depth camera, and generating three-dimensional point cloud data of the face based on the RGB image and the depth image; inputting the three-dimensional point cloud data of the face into a trained face segmentation model to obtain a plurality of facial feature regions, where the plurality of the facial feature regions include eyebrow regions, eye regions, a nose region, and a mouth region; and acquiring an association relationship between the plurality of the facial feature regions and key acupoint points, and determining locations of the facial acupoints based on the association relationship and the plurality of the facial feature regions.

The present invention provides a facial acupoint locating method, an acupuncture method, an acupuncture robot, and a storage medium. The facial acupoint locating method includes: collecting an RGB image and a depth image of a face by using a depth camera, and generating three-dimensional point cloud data of the face based on the RGB image and the depth image; inputting the three-dimensional point cloud data of the face into a trained face segmentation model to obtain a plurality of facial feature regions, where the plurality of the facial feature regions include eyebrow regions, eye regions, a nose region, and a mouth region; and acquiring an association relationship between the plurality of the facial feature regions and key acupoint points, and determining locations of the facial acupoints based on the association relationship and the plurality of the facial feature regions.

A method is provided for supporting a robot in response to a contingency event. The method includes detecting the contingency event during travel of the robot on a route to a destination. In response, the method includes determining a position of the robot, and accessing information about alternate destinations associated with the route. The method includes selecting an alternate destination from the alternate destinations based on a time to travel from the position of the robot to the alternate destination, and the information. And the method includes outputting an indication of the alternate destination for use in at least one of guidance, navigation or control of the robot to the alternate destination.

Systems and methods for controlling self-driving robots are provided. Data from a plurality of sensors, associated with a robotic device, is received by a plurality of neural network models. Each neural network model of the plurality of neural network models receives a subset of the data from the plurality of sensors. The plurality of neural network models generate, based on the data from the plurality of sensors, a plurality of outputs. Each output of the plurality of outputs is generated by a particular neural network model of the plurality of neural network models. Each output corresponds to a respective subset of data received by the particular neural network model of the plurality of neural network models. Thereafter, an output from the plurality of outputs is selected, where the output includes data for controlling the robotic device. The robotic device is then controlled based on the output.

Systems and methods for controlling self-driving robots are provided. Data from a plurality of sensors, associated with a robotic device, is received by a plurality of neural network models. Each neural network model of the plurality of neural network models receives a subset of the data from the plurality of sensors. The plurality of neural network models generate, based on the data from the plurality of sensors, a plurality of outputs. Each output of the plurality of outputs is generated by a particular neural network model of the plurality of neural network models. Each output corresponds to a respective subset of data received by the particular neural network model of the plurality of neural network models. Thereafter, an output from the plurality of outputs is selected, where the output includes data for controlling the robotic device. The robotic device is then controlled based on the output.

In at least some implementations, a method for determining range for a vehicle connected to a trailer includes determining connection of a trailer to the vehicle, determining at least one trailer parameter relating to a size, shape or a weight of the trailer, and determining a reduction in vehicle range as a function of the at least one trailer parameter.

A modular vehicle management system includes a vehicle management board configured to interface with any of a plurality of vehicle-specific boards. The vehicle management board includes connectors configured to receive operational data from a vehicle-specific board and to send control data to the vehicle-specific board. The vehicle management board includes processors configured to receive the operational data, generate the control data, and send the control data to the vehicle specific board. The vehicle-specific board includes connectors configured to interface with computing devices, connectors configured to interface with the connectors on the vehicle management board to convey operational data and receive control data, and connectors configured to interface with controllers of the vehicle.

A modular vehicle management system includes a vehicle management board configured to interface with any of a plurality of vehicle-specific boards. The vehicle management board includes connectors configured to receive operational data from a vehicle-specific board and to send control data to the vehicle-specific board. The vehicle management board includes processors configured to receive the operational data, generate the control data, and send the control data to the vehicle specific board. The vehicle-specific board includes connectors configured to interface with computing devices, connectors configured to interface with the connectors on the vehicle management board to convey operational data and receive control data, and connectors configured to interface with controllers of the vehicle.