Learning Ground Traversability from Simulations

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

Electronic phase separation near the superconductor-insulator transition of Nd1+xBa2−xCu3O7−δ thin films studied by an electric-field-induced doping effect

We report a detailed study of the transport properties of Nd(1+x)Ba(2-x)Cu(3)O(7-delta) thin films with doping changed by field effect. The data cover the whole superconducting to insulating transition and show remarkable Similarities with the effect of chemical doping in high critical temperature superconductors. The results suggest that the add-on of carriers is accompanied by an electronic phase separation, independent on the details of the doping mechanism

Challenges in Visual Anomaly Detection for Mobile Robots

We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.Comment: Workshop paper presented at the ICRA 2022 Workshop on Safe and Reliable Robot Autonomy under Uncertainty https://sites.google.com/umich.edu/saferobotautonomy/hom

Electric field effect and superconducting–insulating transition in ‘123’ cuprate superconductors

The physics of high critical temperature superconductors (HTS) remains a fascinating but undisclosed issue in condensed matter. One of the most interesting topics is the transition from the insulating phase of the parent compound, having long range antiferromagnetic order, to the superconducting phase. A method to investigate in detail the superconducting to insulating (SIT) transition in HTS is to control the doping of the CuO(2) planes in a fine way. Here, by using the electric field effect on thin Nd(1)Ba(2)Cu(3)O(7) films, we present a study of the HTS phase diagram close to the SIT with unprecedented detail. By virtue of these data, we will show that doping of holes in samples located at the boundary separating the superconducting and insulating regions produces changes in the transport characteristic consistent with an electronic phase separation scenario. Some consequences of these data are the failure of standard 2D quantum scaling theory and the possible coexistence of superconducting and weakly insulating phases in this region of the phase diagram. A continuous transition between the two competing phases as a function of doping place evident constraints on the mechanism of superconductivity

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.Comment: 14 pages, 2 figures, 4 listing