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In recent years, automatic classifiers of image cutouts (also called "stamps") have shown to be key for fast supernova discovery. The Vera C. Rubin Observatory will distribute about ten million alerts with their respective stamps each night, enabling the discovery of approximately one million supernovae each year. A growing source of confusion for these classifiers is the presence of satellite glints, sequences of point-like sources produced by rotating satellites or debris. The currently planned Rubin stamps will have a size smaller than the typical separation between these point sources. Thus, a larger field of view stamp could enable the automatic identification of these sources. However, the distribution of larger stamps would be limited by network bandwidth restrictions. We evaluate the impact of using image stamps of different angular sizes and resolutions for the fast classification of events (AGNs, asteroids, bogus, satellites, SNe, and variable stars), using data from the Zwicky Transient Facility. We compare four scenarios: three with the same number of pixels (small field of view with high resolution, large field of view with low resolution, and a multi-scale proposal) and a scenario with the full stamp that has a larger field of view and higher resolution. Compared to small field of view stamps, our multi-scale strategy reduces misclassifications of satellites as asteroids or supernovae, performing on par with high-resolution stamps that are 15 times heavier. We encourage Rubin and its Science Collaborations to consider the benefits of implementing multi-scale stamps as a possible update to the alert specification., Comment: Accepted for publication in ApJL

This paper is one in a series reporting results from small telescope observations of variable young stars. Here, we study the repeating outbursts of three likely Be stars based on long-term optical, near-infrared, and mid-infrared photometry for all three objects, along with follow-up spectra for two of the three. The sources are characterised as rare, truly regularly outbursting Be stars. We interpret the photometric data within a framework for modelling light curve morphology, and find that the models correctly predict the burst shapes, including their larger amplitudes and later peaks towards longer wavelengths. We are thus able to infer the start and end times of mass loading into the circumstellar disks of these stars. The disk sizes are typically 3-6 times the areas of the central star. The disk temperatures are ~40%, and the disk luminosities are ~10% of those of the central Be star, respectively. The available spectroscopy is consistent with inside-out evolution of the disk. Higher excitation lines have larger velocity widths in their double-horned shaped emission profiles. Our observations and analysis support the decretion disk model for outbursting Be stars., Comment: Accepted for publication by MNRAS. 20 pages, 11 figures, 2 tables

Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their body pose and by making steps. In this paper, we present a state estimation approach for four-legged robots with non-steerable wheels that enables hybrid driving-stepping locomotion capabilities. We formulate a Kalman Filter (KF) for state estimation that integrates driven wheels into the filter equations and estimates the robot state (position and velocity) as well as the contribution of driving with wheels to the above state. Our estimation approach allows us to use the control framework of the Mini Cheetah quadruped robot with minor modifications. We tested our approach on this robot that we augmented with actively driven wheels in simulation and in the real world. The experimental results are available at https://www.ais.uni-bonn.de/%7Ehosseini/se-dsq ., Comment: Accepted final version. IEEE International Robotic Computing (IRC), Naples, Italy, December 2022

The study of short-term variability properties in AGN jets has the potential to shed light on their particle acceleration and emission mechanisms. We report results from a four-day coordinated multi-wavelength campaign on the highly-peaked blazar (HBL) Mkn 421 in 2019 January. We obtained X-ray data from AstroSAT, BVRI photometry with the Whole Earth Blazar Telescope (WEBT), and TeV data from FACT to explore short-term multi-wavelength variability in this HBL. The X-ray continuum is rapidly variable on time-scales of tens of ks. Fractional variability amplitude increases with energy across the synchrotron hump, consistent with previous studies; we interpret this observation in the context of a model with multiple cells whose emission spectra contain cutoffs that follow a power-law distribution. We also performed time-averaged and time-resolved (time-scales of 6 ks) spectral fits; a broken power-law model fits all spectra well; time-resolved spectral fitting reveals the usual hardening when brightening behaviour. Intra-X-ray cross correlations yield evidence for the 0.6-0.8 keV band to likely lead the other bands by an average of 4.6 +- 2.6 ks, but only during the first half of the observation. The source displayed minimal night-to-night variability at all wavebands thus precluding significant interband correlations during our campaign. The broadband SED is modeled well with a standard one-zone leptonic model, yielding jet parameters consistent with those obtained from previous SEDs of this source., Comment: This article has been accepted for publication in The Monthly Notices of the Royal Astronomical Society (2022), and is published in Volume 513, Issue 2, pp.1662-1679. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved. 19 pages; 12 figures

In this paper, we propose a novel Deep Reinforcement Learning approach to address the mapless navigation problem, in which the locomotion actions of a humanoid robot are taken online based on the knowledge encoded in learned models. Planning happens by generating open-loop trajectories in a learned latent space that captures the dynamics of the environment. Our planner considers visual (RGB images) and non-visual observations (e.g., attitude estimations). This confers the agent upon awareness not only of the scenario, but also of its own state. In addition, we incorporate a termination likelihood predictor model as an auxiliary loss function of the control policy, which enables the agent to anticipate terminal states of success and failure. In this manner, the sample efficiency of the approach for episodic tasks is increased. Our model is evaluated on the NimbRo-OP2X humanoid robot that navigates in scenes avoiding collisions efficiently in simulation and with the real hardware., Comment: Accepted for IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Studying rotational variability of young stars is enabling us to investigate a multitude of properties of young star-disk systems. We utilise high cadence, multi-wavelength optical time series data from the Hunting Outbursting Young Stars citizen science project to identify periodic variables in the Pelican Nebula (IC5070). A double blind study using nine different period-finding algorithms was conducted and a sample of 59 periodic variables was identified. We find that a combination of four period finding algorithms can achieve a completeness of 85% and a contamination of 30% in identifying periods in inhomogeneous data sets. The best performing methods are periodograms that rely on fitting a sine curve. Utilising GaiaEDR3 data, we have identified an unbiased sample of 40 periodic YSOs, without using any colour or magnitude selections. With a 98.9% probability we can exclude a homogeneous YSO period distribution. Instead we find a bi-modal distribution with peaks at three and eight days. The sample has a disk fraction of 50%, and its statistical properties are in agreement with other similarly aged YSOs populations. In particular, we confirm that the presence of the disk is linked to predominantly slow rotation and find a probability of 4.8$\times$10$^{-3}$ that the observed relation between period and presence of a disk has occurred by chance. In our sample of periodic variables, we also find pulsating giants, an eclipsing binary, and potential YSOs in the foreground of IC5070., Comment: 25 pages, 14 figures, 5 tables, accepted for publication in MNRAS

Bipedal walking is one of the most difficult but exciting challenges in robotics. The difficulties arise from the complexity of high-dimensional dynamics, sensing and actuation limitations combined with real-time and computational constraints. Deep Reinforcement Learning (DRL) holds the promise to address these issues by fully exploiting the robot dynamics with minimal craftsmanship. In this paper, we propose a novel DRL approach that enables an agent to learn omnidirectional locomotion for humanoid (bipedal) robots. Notably, the locomotion behaviors are accomplished by a single control policy (a single neural network). We achieve this by introducing a new curriculum learning method that gradually increases the task difficulty by scheduling target velocities. In addition, our method does not require reference motions which facilities its application to robots with different kinematics, and reduces the overall complexity. Finally, different strategies for sim-to-real transfer are presented which allow us to transfer the learned policy to a real humanoid robot., Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 2021

For several years, high development and production costs of humanoid robots restricted researchers interested in working in the field. To overcome this problem, several research groups have opted to work with simulated or smaller robots, whose acquisition costs are significantly lower. However, due to scale differences and imperfect simulation replicability, results may not be directly reproducible on real, adult-sized robots. In this paper, we present the NimbRo-OP2X, a capable and affordable adult-sized humanoid platform aiming to significantly lower the entry barrier for humanoid robot research. With a height of 135 cm and weight of only 19 kg, the robot can interact in an unmodified, human environment without special safety equipment. Modularity in hardware and software allow this platform enough flexibility to operate in different scenarios and applications with minimal effort. The robot is equipped with an on-board computer with GPU, which enables the implementation of state-of-the-art approaches for object detection and human perception demanded by areas such as manipulation and human-robot interaction. Finally, the capabilities of the NimbRo-OP2X, especially in terms of locomotion stability and visual perception, are evaluated. This includes the performance at RoboCup 2018, where NimbRo-OP2X won all possible awards in the AdultSize class.

In this paper, we propose a novel approach to solve the 3D non-rigid registration problem from RGB images using Convolutional Neural Networks (CNNs). Our objective is to find a deformation field (typically used for transferring knowledge between instances, e.g., grasping skills) that warps a given 3D canonical model into a novel instance observed by a single-view RGB image. This is done by training a CNN that infers a deformation field for the visible parts of the canonical model and by employing a learned shape (latent) space for inferring the deformations of the occluded parts. As result of the registration, the observed model is reconstructed. Because our method does not need depth information, it can register objects that are typically hard to perceive with RGB-D sensors, e.g. with transparent or shiny surfaces. Even without depth data, our approach outperforms the Coherent Point Drift (CPD) registration method for the evaluated object categories., Comment: Accepted final version. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

A room-temperature infrared spectroscopy study of elemental tellurium at pressures up to 8.44 GPa in the energy range 0.015-2 eV is reported. Optical signatures of the high-pressure polymorphs are investigated and compared to the results of density-functional band-structure calculations. A Drude peak is first seen in the optical conductivity around 3.5 GPa indicating a semiconductor-to-metal transition within trigonal Te-I. A sharp increase in the Drude spectral weight and dc-conductivity around 4.3 GPa signals the transformation toward the triclinic Te-II polymorph. An absorption peak around 0.15 eV appears above 5 GPa concomitant with the gradual transformation of Te-II into the structurally similar but incommensurately modulated Te-III. Microscopically, this peak can only be reproduced within a sufficiently large commensurate approximant, suggesting the low-energy optical response as a fingerprint of the structural modulation., Comment: 7 pages, 5 figures

Employing high-pressure infrared spectroscopy we unveil the Weyl semimetal phase of elemental Te and its topological properties. The linear frequency dependence of the optical conductivity provides clear evidence for metallization of trigonal tellurium (Te-I) and the linear band dispersion above 3.0 GPa. This semimetallic Weyl phase can be tuned by increasing pressure further: a kink separates two linear regimes in the optical conductivity (at 3.7 GPa), a signature proposed for Type-II Weyl semimetals with tilted cones; this however reveals a different origin in trigonal tellurium. Our density-functional calculations do not reveal any significant tilting and suggest that Te-I remains in the Type-I Weyl phase, but with two valence bandsin the vicinity of the Fermi level. Their interplay giving rise to the peculiar optical conductivity behavior with more than one linear regime. Pressure above 4.3 GPa stabilizes the more complex Te-II and Te-III polymorphs, which are robust metals., Comment: 9 pages, 7 figures

Individual and team capabilities are challenged every year by rule changes and the increasing performance of the soccer teams at RoboCup Humanoid League. For RoboCup 2019 in the AdultSize class, the number of players (2 vs. 2 games) and the field dimensions were increased, which demanded for team coordination and robust visual perception and localization modules. In this paper, we present the latest developments that lead team NimbRo to win the soccer tournament, drop-in games, technical challenges and the Best Humanoid Award of the RoboCup Humanoid League 2019 in Sydney. These developments include a deep learning vision system, in-walk kicks, step-based push-recovery, and team play strategies.

In our work we have studied a two-dimensional suspension of finite-size Vicsek hard-disks, whose time evolution follows an event-driven dynamics between subsequent time steps. Having compared its collective behaviour with the one expected for a system of scalar Vicsek point-like particles, we have analysed the effect of considering two possible bouncing rules between the disks: a Vicsek-like rule and a pseudo-elastic one, focusing on the order-disorder transition. Next, we have added to the two-dimensional suspension of hard-disk Vicsek particles disk-like passive obstacles of two types: either fixed in space or moving according to the same event-driven dynamics. We have performed a detailed analysis of the particles' collective behaviour observed for both fixed and moving obstacles. In the fixed obstacles case, we have observed formation of clusters at low noise, in agreement with previous studies. When using moving passive obstacles, we found that that order of active particles is better destroyed as the drag of obstacles increases. In the no drag limit an interesting result was found: introduction of low drag passive particles can lead in some cases to a more ordered state of active flocking particles than what they show in bulk.

In human-made scenarios, robots need to be able to fully operate objects in their surroundings, i.e., objects are required to be functionally grasped rather than only picked. This imposes very strict constraints on the object pose such that a direct grasp can be performed. Inspired by the anthropomorphic nature of humanoid robots, we propose an approach that first grasps an object with one hand, obtaining full control over its pose, and performs the functional grasp with the second hand subsequently. Thus, we develop a fully autonomous pipeline for dual-arm functional regrasping of novel familiar objects, i.e., objects never seen before that belong to a known object category, e.g., spray bottles. This process involves semantic segmentation, object pose estimation, non-rigid mesh registration, grasp sampling, handover pose generation and in-hand pose refinement. The latter is used to compensate for the unpredictable object movement during the first grasp. The approach is applied to a human-like upper body. To the best knowledge of the authors, this is the first system that exhibits autonomous bimanual functional regrasping capabilities. We demonstrate that our system yields reliable success rates and can be applied on-line to real-world tasks using only one off-the-shelf RGB-D sensor., Comment: Accepted for IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), Toronto, Canada, 2019