Abstract: A learning system for a navigating robot includes: a navigation module including: a first policy configured to determine actions for moving the navigating robot and navigating from a starting location to an ending location based on images from a camera of the navigating robot; and a second policy configured to, based on a representation of an environment generated by the navigating robot, determine actions for moving the navigating robot from waypoint locations between the starting location and the ending location to a plurality of subgoal locations without any images from the camera; and a representation module configured to: selectively learn the representation during movement via the first policy based on the representation at previous times, images from the camera, and actions determined by the first policy at previous times; and provide the representation to the second policy.

Abstract: A method includes: performing unsupervised pre-training of a model, the model including and a decoder including: obtaining a first image and a second image under different conditions or from different viewpoints; encoding, by the encoder, the first image into a representation of the first image and the second image into a representation of the second image; transforming the representation of the first image into a transformed representation; decoding, by the decoder, the transformed representation into a reconstructed image, where the transforming of the representation of the first image and the decoding of the transformed representation is based on the representation of the first image and the representation of the second image; and adjusting one or more parameters of at least one of the encoder and the decoder based on minimizing a loss; and fine-tuning the model, initialized with a set of task specific encoder parameters, for a geometric vision task.

Abstract: A computer-implemented method of determining a position of a portable electronic device in an indoor environment includes: at a first rate, updating an absolute position of a portable electronic device within the indoor environment based on at least one of radio signal data and magnetic field data captured using the portable electronic device; at a second rate that is different than the first rate, selectively updating an estimated displacement of the portable electronic device within the indoor environment, the updating the estimated displacement comprising generating an estimated displacement, by a neural network module, based on inertial sensor data of the portable electronic device; and determining a present position of the portable electronic device within the indoor environment by updating a previous position based on at least one of (a) the estimated displacement and (b) the absolute position.

Abstract: A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.

Abstract: A computer-implemented method of determining a position of a portable electronic device in an indoor environment includes: at a first rate, updating an absolute position of a portable electronic device within the indoor environment based on at least one of radio signal data and magnetic field data captured using the portable electronic device; at a second rate that is different than the first rate, selectively updating an estimated displacement of the portable electronic device within the indoor environment, the updating the estimated displacement comprising generating an estimated displacement, by a neural network module, based on inertial sensor data of the portable electronic device; and determining a present position of the portable electronic device within the indoor environment by updating a previous position based on at least one of (a) the estimated displacement and (b) the absolute position.

Abstract: A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.

Abstract: A computer-implemented system and method generate a representation of travel costs for an individual traveler. The method includes computing a fixed community cost component attributable to each traveler in a community, per time period, computing a fixed individual cost component for the individual traveler, per time period, and for each of a set of time periods, computing a variable individual cost component for the individual traveler. For each of the time periods in the set of time periods, the fixed community cost, fixed individual cost, and variable individual cost components are aggregated into an aggregated cost. A representation is generated for displaying the aggregated costs for the set of time periods to the individual traveler, in which the three cost components are identified.

Abstract: Methods and apparatus to provide cluster-based fingerprinting algorithms are described. In one embodiment, m closest fingerprints from k fingerprints are determined. The k fingerprints may be determined as closest fingerprints to a scan s in a signal strength domain, where the scan s is of an environment surrounding a wireless device. The center of mass of the m fingerprints may be used to determine a location estimation for the wireless device. Other embodiments are also described.

Abstract: A motion stabilizing techniques are described. A frame of video is represented at least by frame position information. Savitzky-Golay filtering may be applied on frame position information. The resulting filtered video may have reduced visible jitteriness.

Abstract: A motion stabilizing techniques are described. A frame of video is represented at least by frame position information. Savitzky-Golay filtering may be applied on frame position information. The resulting filtered video may have reduced visible jitteriness.

Abstract: Methods and apparatus to provide cluster-based fingerprinting algorithms are described. In one embodiment, m closest fingerprints from k fingerprints are determined. The k fingerprints may be determined as closest fingerprints to a scan s in a signal strength domain, where the scan s is of an environment surrounding a wireless device. The center of mass of the m fingerprints may be used to determine a location estimation for the wireless device. Other embodiments are also described.