Abstract: A method of monitoring a device battery for predictively detecting a fault in the device battery in a technical device includes providing a historical temporal operating variable curve of several operating variables of a specific device battery, and providing a predicted temporal operating variable curve dependent on a usage pattern model, which is dependent on a usage behavior characterizing a type of use of the device battery. The method further includes determining a time series of input variable vectors each with elements which comprise one or more operating variables and/or one or more variables derived therefrom for a time step. A time series includes time steps from the historical and predicted operating variable curves. The method further includes evaluating a data-based anomaly prediction model comprising a data-based time series transformer model and a data-based prediction model.

Abstract: A method for training a machine learning model to recognize an object topology of an object from an image of the object. The method includes: obtaining a 3D model of the object; determining a descriptor component value for each vertex of the grid; generating training data image pairs each having a training input image and a target image. The target image is generated by determining the vertex positions in the training input image; assigning the descriptor component value determined for the vertex at the vertex position to the position in the target image; and adapting at least some of the descriptor component values assigned to the positions in the target image or adding descriptor component values to the positions of the target image.

Abstract: A method for controlling a robot to pick up an object in various positions. The method includes: defining a plurality of reference points on the object; mapping a first camera image of the object in a known position onto a first descriptor image; identifying the descriptors of the reference points from the first descriptor image; mapping a second camera image of the object in an unknown position onto a second descriptor image; searching the identified descriptors of the reference points in the second descriptor image; ascertaining the positions of the reference points in the three-dimensional space in the unknown position from the found positions; and ascertaining a pickup pose of the object for the unknown position from the ascertained positions of the reference points.

Abstract: A method for controlling a robot for manipulating, in particular picking up, an object. The method includes: creating an image which depicts the object; generating a manipulation-quality image from the image, in which, for each pixel which represents a point on the surface of the object, the pixel value of the pixel provides an assessment of how well the object may be manipulated at the point; recording descriptors of points of the object which should be used during the manipulation and/or of points which should be avoided during the manipulation; mapping the image onto a descriptor image; generating a manipulation-preference image by comparing the recorded descriptors of points to the descriptor image; selecting a point for manipulating the object taking into account the pixel values of the manipulation-quality image and the pixel values of the manipulation-preference image; and controlling the robot to manipulate the object at the selected point.

Abstract: A method for training a machine learning model for generating descriptor images for images of one or more objects. The method includes recording multiple camera images, each showing one or more objects, and, for each camera image, generating one or more augmented versions of the camera image by applying a respective augmentation to the camera image for each augmented version of the camera image, wherein the augmentation comprises a change in position of pixel values of the camera image, generating pairs of training images each including the camera image and an augmentation of the camera image or two augmented versions of the camera image; and training the machine learning model with contrastive loss using the pairs of training images.

Abstract: A method for training a machine learning model for generating descriptor images for images of one or of multiple objects. The method includes: formation of pairs of images which show the one or the multiple objects from different perspectives; generation, for each image pair, using the machine learning model, of a first descriptor image for the first image, and of a second descriptor image for the second image, which assigns descriptors to points of the one or multiple objects shown in the second image; sampling, for each image pair, of descriptor pairs, which include in each case a first descriptor from the first descriptor image and a second descriptor from the second descriptor image, which are assigned to the same point, and the adaptation of the machine learning method for reducing a loss.

Abstract: A computer-implemented method for operating a motor vehicle, in particular an electrically drivable motor vehicle, depending on a predicted state of health of an electrical energy store, in particular a vehicle battery. The method includes: providing vehicle parameters which influence the state of health of the electrical energy store; predicting the vehicle parameters at a prediction point in time; ascertaining the predicted state of health depending on the predicted vehicle parameters with the aid of a data-based state of health model which is trained to output a state of health of the electrical energy store depending on the vehicle parameters; and signaling the predicted state of health.

Abstract: A method for training a machine learning model for recognizing an object topology of an object from an image of the object. The method includes obtaining a 3D model of the object, wherein the 3D model comprises a mesh of vertices connected by edges, wherein each edge has a weight which specifies proximity of two vertices connected by the edge in the object; determining a descriptor for each vertex of the mesh by searching descriptors for the vertices which minimize the sum, over pairs of connected vertices, of distances between the descriptors of the pair of vertices weighted by the weight of the edge between the pair of vertices; generating training data image pairs, wherein each training data image pair comprises a training input image showing the object and a target image; and training the machine learning model by supervised learning using the training data image pairs as training data.

Abstract: A method for controlling a robot. The method includes performing demonstrations and descriptor images for the demonstrations from a point of view of the robot of the object; selecting a set of feature points, wherein the feature points are selected by searching an optimum of an objective function which rewards selected feature points being visible in the descriptor images; training a robot control model using the demonstrations and controlling the robot for a control scene with the object by determining a descriptor image of the object, locating the selected set of feature points in the descriptor image of the object; determining Euclidean coordinates of the located feature points; estimating a pose from the determined Euclidean coordinates; and controlling the robot to handle the object by means of the robot control model with the estimated pose.

Abstract: A method for generating training data for supervised learning for training a neural network to identify, from digital images of objects, locations of the objects for interacting with the objects. The method includes: acquiring, for each training object, at least one digital reference image and a plurality of further images of the training object; for each training object, specifying a location of the training object, mapping the at least one reference image onto a descriptor image, identifying descriptors of the specified location, mapping the further images of the training object onto further descriptor images, and determining locations in the further images by locating points in the further images, the descriptors of which in the further descriptor images correspond to the specified descriptors of the at least one specified location; and generating the training data for supervised learning by marking the determined locations for the further images of the training objects.

Abstract: A method for controlling a robotic device. The method includes providing demonstrations for carrying out a skill by the robot, each demonstration including a robot pose, an acting force as well as an object pose for each point in time of a sequence of points in time, ascertaining an attractor demonstration for each demonstration, training a task-parameterized robot trajectory model for the skill based on the attractor trajectories and controlling the robotic device according to the task-parameterized robot trajectory model.

Abstract: A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.

Abstract: A method for controlling a robot for picking up an object. The method includes: receiving a camera image of an object; ascertaining an image region in the camera image showing an area of the object where it may not be picked up, by conveying the camera image to a machine learning model which is trained to allocate values to regions in camera images that represent whether the regions show areas of an object where it may not be picked up, allocating the ascertained image region to a spatial region; and controlling the robot to grasp the object in a spatial region other than the ascertained spatial region.

Abstract: A method for training a machine learning model to recognize an object topology of an object from an image of the object. The method includes: obtaining a 3D model of the object; determining a descriptor component value for each vertex of the grid; generating training data image pairs each having a training input image and a target image. The target image is generated by determining the vertex positions in the training input image; assigning the descriptor component value determined for the vertex at the vertex position to the position in the target image; and adapting at least some of the descriptor component values assigned to the positions in the target image or adding descriptor component values to the positions of the target image.

Abstract: A method for controlling a robot to pick up an object in various positions. The method includes: defining a plurality of reference points on the object; mapping a first camera image of the object in a known position onto a first descriptor image; identifying the descriptors of the reference points from the first descriptor image; mapping a second camera image of the object in an unknown position onto a second descriptor image; searching the identified descriptors of the reference points in the second descriptor image; ascertaining the positions of the reference points in the three-dimensional space in the unknown position from the found positions; and ascertaining a pickup pose of the object for the unknown position from the ascertained positions of the reference points.

Abstract: A method for controlling a robotic device, in which a composite robot trajectory model made up of robot trajectory models of the movement skills is generated for a sequence plan for a task to be carried out by the robot including a sequence of movement skills and primitive actions to be carried out, and the robot is controlled, if after one movement skill according to the sequence plan one or multiple primitive action(s) is/are to be executed before the next movement skill, by interrupting the control of the robot according to the composite robot trajectory model after carrying out the movement skill, and by executing the one or multiple primitive action(s) and then resuming the control of the robot according to the composite robot trajectory model.

Abstract: A device for and method of operating a machine. The method includes providing a sequence of skills of the machine for executing a task, selecting a sequence of states from a plurality of sequences of states, depending on a likelihood, wherein the likelihood is determined depending on a transition probability from a final state of a first sub-sequence of states of the sequence of states for a first skill in the sequence of skills to an initial state of a second sub-sequence of states of the sequence of states for a second skill in the sequence of skills.

Abstract: A method for ascertaining an approximation and/or a prognosis for the true degradation state of a rechargeable battery. The method includes: providing a time sequence of values of the degradation state ascertained using measuring technology for past points in time; providing a trained HMM, which indicates, as a function of the true degradation state, at which probability during the ascertainment using measuring technology which particular value of the degradation state is monitored, and at which probability the true degradation state is maintained for what length of time, and/or at which probability this true degradation state transitions to which worse degradation state in the next time step; from the monitored time sequence and the HMM, the most probable characteristic of the true degradation state in the past that is in agreement with the monitored time sequence is ascertained; the desired approximation and/or prognosis is evaluated based on the most probable characteristic.

Abstract: A computer-implemented method for operating a motor vehicle, in particular an electrically drivable motor vehicle, depending on a predicted state of health of an electrical energy store, in particular a vehicle battery. The method includes: providing vehicle parameters which influence the state of health of the electrical energy store; predicting the vehicle parameters at a prediction point in time; ascertaining the predicted state of health depending on the predicted vehicle parameters with the aid of a data-based state of health model which is trained to output a state of health of the electrical energy store depending on the vehicle parameters; and signaling the predicted state of health.

Abstract: A method for planning a manipulation task of an agent, particularly a robot. The method includes: learning a number of manipulation skills wherein a symbolic abstraction of the respective manipulation skill is generated; determining a concatenated sequence of manipulation skills selected from the number of learned manipulation skills based on their symbolic abstraction so that a given goal specification indicating a given complex manipulation task is satisfied; and executing the sequence of manipulation skills.