A method for optimizing production of sheet-metal parts, the production comprising cutting out and singularizing the sheet-metal parts and bending the sheet-metal parts, wherein the method includes: (A) training a neural network, which is executed on a Monte Carlo tree search framework, by means of supervised learning and self-play with reinforcement learning; (B) recording constraints for the sheet-metal parts, the constraints comprising geometric data of the sheet-metal parts; (C) creating an optimized production schedule by way of the neural network; and (D) outputting the production schedule.

A method creates numerical control data sets for controlling machine tools. The control data sets are read from the machine tools. A first component data set representing a first component design model is received. A first numerical control data set is created for the first component data set using control program generation software, having an assessment routine using a trained machine learning algorithm with settable parameters. A first additional training data set is compiled from the component data set and the created numerical control data set. The first additional training data set is output to a training database. The machine learning algorithm is updated by setting usage-environment-specific values for the parameters determined by training the machine learning training algorithm using the training database.

Disclosed herein are a sound sleep assistance apparatus, a sound sleep assistance method, and a sound sleep assistance system. According to an embodiment, there is provided a sound sleep assistance apparatus for assisting the sound sleep of a user by communicating with a sleep pad, the sound sleep assistance apparatus including: a communication interface configured to communicate with the sleep pad that acquires the physiological index information of the user while the user lies down; and a controller configured to determine the sleep stage of the user based on the physiological index information, and to provide a sound source corresponding to the determined sleep stage.

In order to control a technical system using a control model, a transformation function is provided for reducing and/or obfuscating operating data of the technical system so as to obtain transformed operating data. In addition, the control model is generated by a model generator according to a first set of operating data of the technical system. In an access domain separated from the control model, a second set of operating data of the technical system is recorded and transformed by the transformation function into a transformed second set of operating data which is received by a model execution system. The control model is then executed by the model execution system, by supplying the transformed second set of operating data in an access domain separated from the second set of operating data, control data being derived from the transformed second set of operating data.

Apparatuses, systems, and techniques to place one or more objects in a location and orientation. In at least one embodiment, one or more circuits are to use one or more neural networks to cause one or more autonomous devices to place one or more objects in a location and orientation based, at least in part, on one or more images of the location and orientation.

Techniques for generating simulations for evaluating a performance of a controller of an autonomous vehicle are described. A computing system may evaluate the performance of the controller to navigate the simulation and respond to actions of one or more objects (e.g., other vehicles, bicyclists, pedestrians, etc.) in a simulation. Actions of the objects in the simulation may be controlled by the computing system (e.g., by an artificial intelligence) and/or one or more users inputting object controls, such as via a user interface. The computing system may calculate performance metrics associated with the actions performed by the vehicle in the simulation as directed by the autonomous controller. The computing system may utilize the performance metrics to verify parameters of the autonomous controller (e.g., validate the autonomous controller) and/or to train the autonomous controller utilizing machine learning techniques to bias toward preferred actions.

A depth image of an object can be input to a deep neural network to determine a first four degree-of-freedom pose of the object. The first four degree-of-freedom pose and a three-dimensional model of the object can be input to a silhouette rendering program to determine a first two-dimensional silhouette of the object. A second two-dimensional silhouette of the object can be determined based on thresholding the depth image. A loss function can be determined based on comparing the first two-dimensional silhouette of the object to the second two-dimensional silhouette of the object. Deep neural network parameters can be optimized based on the loss function and the deep neural network can be output.

A method for bypassing impassable objects by a robot through the use of artificial intelligence. A reliable and low-cost bypassing of obstacles taking account of data privacy aspects is achieved in that in the event of a collision of the robot with an obstacle, an optical original recording of the obstacle is produced, artificial duplicates being generated from the original recording, the duplicates being used to train the artificial intelligence. A system has a robot and an IT infrastructure configured to execute the method.

There are provided systems, methods, and apparatus, for optimizing a policy controller to control a robotic agent that interacts with an environment to perform a robotic task. One of the methods includes optimizing the policy controller using a neural network that generates numeric embeddings of images of the environment and a demonstration sequence of demonstration images of another agent performing a version of the robotic task.

A method for generating a digital model-based representation of a vehicle. The method includes: receiving sensor data of a plurality of acoustic sensors of a vehicle, wherein the sensor data describes sounds of the vehicle and/or sounds of an environment of the vehicle, and wherein the sensor data has been recorded for a plurality of trips of the vehicle; evaluating the sensor data and the creation of relations between the received sounds of the vehicle and/or the environment and the particular sound-causing statuses of the vehicle and/or the environment; and storing in a model-based representation of the vehicle and/or the environment, the determined relations between the sounds of the vehicle and/or the environment in a model-based representation of the vehicle.