Abstract: Aspects presented herein may enable a passive positioning system, which may be a network entity or node, to be trained to identify multiple moving objects based on using training data for a single object. In one aspect, a network entity receives first RF channel data recorded by a set of devices for a coverage area during a first time period. The network entity trains an ML model based on the set of devices and the first RF channel data. The network entity receives second RF channel data recorded by the set of devices at a second time instance that is outside of the first time period. The network entity computes a number of moving objects in the coverage area at the second time instance based on the second RF channel data using the ML model.

Abstract: A processor-implemented method includes observing an environment via one or more sensors associated with a robotic device. The processor-implemented method also includes generating, via an inference model, a belief of the environment based on data associated with prior actions of the robotic device in the environment. The processor-implemented method further includes controlling the robotic device to perform an action in the environment based on generating the belief.

Abstract: Systems and techniques are provided for unsupervised scene-decompositional normalizing flows. An example process can include obtaining a scene-decompositional model having a normalizing flow neural network architecture. The process can include determining, based on processing data depicting multiple targets in a scene using the scene-decompositional model, a distribution of scene data as a mixture of flows from one or more background components and one or more foreground components. The process can further include identifying, based on processing the distribution of scene data using the scene-decompositional model, a target associated with the one or more foreground components and included in the data depicting the multiple targets in the scene.

Abstract: Certain aspects of the present disclosure provide techniques method for self-supervised training of a machine learning model to predict the location of a device in a spatial environment, such as a spatial environment including multiple discrete planes. An example method generally includes receiving an input data set of scene data. A generator model is trained to map scene data in the input data set to points in three-dimensional space. One or more critic models are trained to backpropagate a gradient to the generator model to push the points in the three-dimensional space to one of a plurality of planes in the three-dimensional space. At least the generator is deployed.

Abstract: Certain aspects of the present disclosure provide techniques for training and inferencing with machine learning localization models. In one aspect, a method, includes training a machine learning model based on input data for performing localization of an object in a target space, including: determining parameters of a neural network configured to map samples in an input space based on the input data to samples in an intrinsic space; and determining parameters of a coupling matrix configured to transport the samples in the intrinsic space to the target space.