A method for connecting a mobile operator terminal to a device to be operated includes outputting a code using an output element associated with the device. The code is transmitted to the mobile operator terminal. At least one piece of information related to the device is determined by decoding the transmitted code using software installed on the mobile operator terminal. A connection from the mobile operator terminal to a control unit associated with the device is then established with the aid of the piece of information related to the device and a piece of information related to the mobile operator terminal is transmitted from the mobile operator terminal to the control unit associated with the device over the established connection. A mobile operator terminal, a device for medical diagnosis or therapy and a system including a mobile operator terminal and a device, are also provided.

Techniques described herein relate to using reduced-dimensionality embeddings generated from robot sensor data to identify predetermined semantic labels that guide robot interaction with objects. In various implementations, obtaining, from one or more sensors of a robot, sensor data that includes data indicative of an object observed in an environment in which the robot operates. The sensor data may be processed utilizing a first trained machine learning model to generate a first embedded feature vector that maps the data indicative of the object to an embedding space. Nearest neighbor(s) of the first embedded feature vector may be identified in the embedding space. Semantic label(s) may be identified based on the nearest neighbor(s). A given grasp option may be selected from enumerated grasp options previously associated with the semantic label(s). The robot may be operated to interact with the object based on the pose and using the given grasp option.

Techniques described herein relate to using reduced-dimensionality embeddings generated from robot sensor data to identify predetermined semantic labels that guide robot interaction with objects. In various implementations, obtaining, from one or more sensors of a robot, sensor data that includes data indicative of an object observed in an environment in which the robot operates. The sensor data may be processed utilizing a first trained machine learning model to generate a first embedded feature vector that maps the data indicative of the object to an embedding space. Nearest neighbor(s) of the first embedded feature vector may be identified in the embedding space. Semantic label(s) may be identified based on the nearest neighbor(s). A given grasp option may be selected from enumerated grasp options previously associated with the semantic label(s). The robot may be operated to interact with the object based on the pose and using the given grasp option.

Techniques described herein relate to using reduced-dimensionality embeddings generated from robot sensor data to identify predetermined semantic labels that guide robot interaction with objects. In various implementations, sensor data obtained from one or more sensors of a robot includes data indicative of an object observed in an environment in which the robot operates. The sensor data is processed utilizing a first trained machine learning model to generate a first embedded feature vector that maps the data indicative of the object to an embedding space. Nearest neighbor(s) of the first embedded feature vector is identified in the embedding space. Semantic label(s) are identified based on the nearest neighbor(s). A given grasp option is selected from enumerated grasp options previously associated with the semantic label(s). The robot is operated to interact with the object based on the pose and using the given grasp option.

Systems, apparatuses, and methods are provided herein for store management. A system for store management comprises: a storage system in a backroom area, a conveyor system, a return sensor system, and a control circuit. The control circuit being configured to: receive an interact request from a customer via a user interface device in a showroom area, cause the conveyor system to retrieve one or more items from the storage system and transport the one or more items to the item interaction area based on the interact request, receive a return request, cause the return sensor system to collect data from the at least one item, determine whether to accept the return request based on the data collected by the return sensor system, and cause the conveyor system to retrieve the at least one item from the customer and make the at least one item available for another customer.

In a method of carrying a munition on a munition launcher platform, the munition launcher platform is provided with a data tag activator and a data tag reader. A munition is attached to the munition launcher platform, the munition being provided with a data tag. An activation signal is transmitted from the data tag activator to the data tag. As a result of receiving the activation signal, the data tag returns a data response to the data tag reader. The receiving of the data response provides the munition launcher platform with an indication that the munition is still attached to the munition launcher platform.

The present application discloses an automatic powder cleaning process and device for wheel bolt holes. The device comprises a vacuum pump, a steady pressure tank, a powder cleaning robot, a powder cleaning gun, a control system and accessories. According to the process and the device, shielded protection is canceled, and powder at the mounting part of a wheel bolt hole is automatically cleaned, so that the process and the device are suitable for various wheel products, and the labor and the labor cost are reduced; the problems that a lot of different types of protective plugs needed for wheels are inconvenient to customize and store and high in cost are avoided.

A method for connecting a mobile operator terminal to a device to be operated includes outputting a code using an output element associated with the device. The code is transmitted to the mobile operator terminal. At least one piece of information related to the device is determined by decoding the transmitted code using software installed on the mobile operator terminal. A connection from the mobile operator terminal to a control unit associated with the device is then established with the aid of the piece of information related to the device and a piece of information related to the mobile operator terminal is transmitted from the mobile operator terminal to the control unit associated with the device over the established connection. A mobile operator terminal, a device for medical diagnosis or therapy and a system including a mobile operator terminal and a device, are also provided.

Techniques described herein relate to using reduced-dimensionality embeddings generated from robot sensor data to identify predetermined semantic labels that guide robot interaction with objects. In various implementations, obtaining, from one or more sensors of a robot, sensor data that includes data indicative of an object observed in an environment in which the robot operates. The sensor data may be processed utilizing a first trained machine learning model to generate a first embedded feature vector that maps the data indicative of the object to an embedding space. Nearest neighbor(s) of the first embedded feature vector may be identified in the embedding space. Semantic label(s) may be identified based on the nearest neighbor(s). A given grasp option may be selected from enumerated grasp options previously associated with the semantic label(s). The robot may be operated to interact with the object based on the pose and using the given grasp option.

An apparatus, method for operating an automation device which includes a processor for directly executing function modules, where a function module selected as a component of an automation solution via a development environment, is converted into a code block via the development environment, where the code block includes a type identifier corresponding to the type of the particular function module, where inputs and outputs of the particular function module are mapped to simultaneously usable processor registers in the code block, a plurality of code blocks is processed by reading-in a particular code block by the processor and subsequently executing the code block to execute the automation solution, and where execution of the code block includes selecting a function unit from a plurality of function units based on the type identifier of the code block and activation of the selected function unit with the processor registers specified in the code block.