Mobile robots require knowledge of the environment, especially of humans located in its vicinity. While the most common approaches for detecting humans involve computer vision, an often overlooked hardware feature of robots for people detection are their 2D range finders. These were originally intended for obstacle avoidance and mapping/SLAM tasks. In most robots, they are conveniently located at a height approximately between the ankle and the knee, so they can be used for detecting people too, and with a larger field of view and depth resolution compared to cameras. In this paper, we present a new dataset for people detection using knee-high 2D range finders called FROG. This dataset has greater laser resolution, scanning frequency, and more complete annotation data compared to existing datasets such as DROW. Particularly, the FROG dataset contains annotations for 100% of its laser scans (unlike DROW which only annotates 5%), 17x more annotated scans, 100x more people annotations, and over twice the distance traveled by the robot. We propose a benchmark based on the FROG dataset, and analyze a collection of state-of-the-art people detectors based on 2D range finder data. We also propose and evaluate a new end-to-end deep learning approach for people detection. Our solution works with the raw sensor data directly (not needing hand-crafted input data features), thus avoiding CPU preprocessing and releasing the developer of understanding specific domain heuristics. Experimental results show how the proposed people detector attains results comparable to the state of the art, while an optimized implementation for ROS can operate at more than 500 Hz.

This paper presents a new approach for 6DoF Direct LiDAR-Inertial Odometry (D-LIO) based on the simultaneous mapping of truncated distance fields on CPU. Such continuous representation (in the vicinity of the points) enables working with raw 3D LiDAR data online, avoiding the need of LiDAR feature selection and tracking, simplifying the odometry pipeline and easily generalizing to many scenarios. The method is based on the proposed Fast Truncated Distance Field (Fast-TDF) method as a convenient tool to represent the environment. Such representation enables i) solving the LiDAR point-cloud registration as a nonlinear optimization process without the need of selecting/tracking LiDAR features in the input data, ii) simultaneously producing an accurate truncated distance field map of the environment, and iii) updating such map at constant time independently of its size. The approach is tested using open datasets, aerial and ground. It is also benchmarked against other state-of-the-art odometry approaches, demonstrating the same or better level of accuracy with the added value of an online-generated TDF representation of the environment, that can be used for other robotics tasks as planning or collision avoidance. The source code is publicly available at this https URL

As the statistical precision of cosmological measurements increases, the accuracy of the theoretical description of these measurements needs to increase correspondingly in order to infer the underlying cosmology that governs the Universe. To this end, we have created the Cosmology Likelihood for Observables in Euclid (CLOE), which is a novel cosmological parameter inference pipeline developed within the Euclid Consortium to translate measurements and covariances into cosmological parameter constraints. In this first in a series of six papers, we describe the theoretical recipe of this code for the Euclid primary probes. These probes are composed of the photometric 3x2pt observables of cosmic shear, galaxy-galaxy lensing, and galaxy clustering, along with spectroscopic galaxy clustering. We provide this description in both Fourier and configuration space for standard and extended summary statistics, including the wide range of systematic uncertainties that affect them. This includes systematic uncertainties such as intrinsic galaxy alignments, baryonic feedback, photometric and spectroscopic redshift uncertainties, shear calibration uncertainties, sample impurities, photometric and spectroscopic galaxy biases, as well as magnification bias. The theoretical descriptions are further able to accommodate both Gaussian and non-Gaussian likelihoods and extended cosmologies with non-zero curvature, massive neutrinos, evolving dark energy, and simple forms of modified gravity. These theoretical descriptions that underpin CLOE will form a crucial component in revealing the true nature of the Universe with next-generation cosmological surveys such as Euclid.

This paper introduces a novel framework for continuous 3D trajectory optimization in cluttered environments, leveraging online neural Euclidean Signed Distance Fields (ESDFs). Unlike prior approaches that rely on discretized ESDF grids with interpolation, our method directly optimizes smooth trajectories represented by fifth-order polynomials over a continuous neural ESDF, ensuring precise gradient information throughout the entire trajectory. The framework integrates a two-stage nonlinear optimization pipeline that balances efficiency, safety and smoothness. Experimental results demonstrate that C-3TO produces collision-aware and dynamically feasible trajectories. Moreover, its flexibility in defining local window sizes and optimization parameters enables straightforward adaptation to diverse user's needs without compromising performance. By combining continuous trajectory parameterization with a continuously updated neural ESDF, C-3TO establishes a robust and generalizable foundation for safe and efficient local replanning in aerial robotics.

This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization. We provide our motivations for our approach and describe our proposal for storing rated social navigation trajectory datasets. Following these guidelines, we compiled a dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presented it to human raters, yielding a total of 4402 rated trajectories after data quality assurance. We also trained an RNN-based baseline metric on the dataset and present quantitative and qualitative results. All data, software, and model weights are publicly available.

Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data.

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long range radar sensors with new RF antenna arrays, which offer digital beam forming. This radar measures independently the distance, speed and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available, so that the user can check the correctness of our driver.

This paper presents a high-efficiency, CPU-only volumetric mapping framework based on a Truncated Signed Distance Field (TSDF). The system incrementally fuses raw LiDAR point-cloud data into a voxel grid using a directional bitmask-based integration scheme, producing dense and consistent TSDF representations suitable for real-time 3D reconstruction. A key feature of the approach is that the processing time per point-cloud remains constant, regardless of the voxel grid resolution, enabling high resolution mapping without sacrificing runtime performance. In contrast to most recent TSDF/ESDF methods that rely on GPU acceleration, our method operates entirely on CPU, achieving competitive results in speed. Experiments on real-world open datasets demonstrate that the generated maps attain accuracy on par with contemporary mapping techniques.

4D millimeter-wave (mmWave) radars are sensors that provide robustness against adverse weather conditions (rain, snow, fog, etc.), and as such they are increasingly being used for odometry and SLAM applications. However, the noisy and sparse nature of the returned scan data proves to be a challenging obstacle for existing point cloud matching based solutions, especially those originally intended for more accurate sensors such as LiDAR. Inspired by visual odometry research around 3D Gaussian Splatting, in this paper we propose using freely positioned 3D Gaussians to create a summarized representation of a radar point cloud tolerant to sensor noise, and subsequently leverage its inherent probability distribution function for registration (similar to NDT). Moreover, we propose simultaneously optimizing multiple scan matching hypotheses in order to further increase the robustness of the system against local optima of the function. Finally, we fuse our Gaussian modeling and scan matching algorithms into an EKF radar-inertial odometry system designed after current best practices. Experiments using publicly available 4D radar datasets show that our Gaussian-based odometry is comparable to existing registration algorithms, outperforming them in several sequences.

This paper presents DLL, a fast direct map-based localization technique using 3D LIDAR for its application to aerial robots. DLL implements a point cloud to map registration based on non-linear optimization of the distance of the points and the map, thus not requiring features, neither point correspondences. Given an initial pose, the method is able to track the pose of the robot by refining the predicted pose from odometry. Through benchmarks using real datasets and simulations, we show how the method performs much better than Monte-Carlo localization methods and achieves comparable precision to other optimization-based approaches but running one order of magnitude faster. The method is also robust under odometric errors. The approach has been implemented under the Robot Operating System (ROS), and it is publicly available.

Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift-diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible. This work uses DD modelling to generate a large synthetic dataset of impedance spectra for a standard TiO2/MAPI/spiro configuration. This dataset trains machine learning (ML) models to predict recombination and ionic parameters from impedance measurements. A Gradient Boosting Regressor, using features from a generalized equivalent circuit, showed the best performance. Interpretative analysis indicates open-circuit impedance experiments best probe recombination losses, while short-circuit conditions are more adequate for extracting ionic features like concentrations and mobilities. The trained ML models were tested on experimental spectra, confirming the inferred physical parameters could reproduce the data. For the studied configuration, predicted ion concentrations were (1.3-3.3) 10^17 cm-3, ion mobilities were (5-7) 10^-11 cm2V-1s-1, and surface recombination velocities were 7-9 and 23-35 m/s. This approach provides insights into the physical information extractable from impedance measurements and paves the way for ML models to unambiguously derive efficiency-determining parameters for solar cells.

This paper presents a simulation framework able of modeling the dynamics of a hanging tether with adjustable length, connecting a UAV to a UGV. The model incorporates the interaction between the UAV, UGV, and a winch, allowing for dynamic tether adjustments based on the relative motion of the robots. The accuracy and reliability of the simulator are assessed through extensive experiments, including comparisons with real-world experiment, to evaluate its ability to reproduce the complex tether dynamics observed in physical deployments. The results demonstrate that the simulation closely aligns with real-world behavior, particularly in constrained environments where tether effects are significant. This work provides a validated tool for studying tethered robotic systems, offering valuable insights into their motion dynamics and control strategies.

The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots. The system was developed to address Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in its surroundings. The multi-robot system developed consists of a heterogeneous robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and UGVplatforms and also present the main algorithmic components of the system: a 3D LIDAR-based mapping and localization module able to work in GPS-denied scenarios; a global planner and a fast local re-planning system for robot navigation; infrared-based perception and robot actuation control for fire extinguishing; and a mission executive and coordination module based on Behavior Trees. The paper finally describes the results obtained during the competition, where the system worked fully autonomously and scored in all the trials performed. The presented system ended in 7th position out of 20 teams in the Challenge3 competition and in 5th position (out of 17 teams) in the Challenge 3 entry to the Grand Finale (Grand Challenge) of MBZIRC 2020 competition.

We combine the Dyson-Schwinger/Bethe-Salpeter equations framework with modern numerical reconstruction methods to derive the three-dimensional and transverse two-dimensional charge distribution of an array of ground-state pseudoscalar and vector mesons from their elastic electromagnetic form factor in the low-momentum region. The charge radii obtained by averaging over the reconstructed charge distributions have been checked to be consistent with those calculated from the slope of the elastic electromagnetic form factor at zero transferred momentum. The capability of the reconstruction procedure for capturing a reliable low-distance charge distribution is discussed and argued to work down to distances of around 0.1 fm, such that it might be potentially applied to extract, {\it e.g.}, mass densities from gravitational form factors.

This work presents an approach to learn path planning for robot social navigation by demonstration. We make use of Fully Convolutional Neural Networks (FCNs) to learn from expert's path demonstrations a map that marks a feasible path to the goal as a classification problem. The use of FCNs allows us to overcome the problem of manually designing/identifying the cost-map and relevant features for the task of robot navigation. The method makes use of optimal Rapidly-exploring Random Tree planner (RRT*) to overcome eventual errors in the path prediction; the FCNs prediction is used as cost-map and also to partially bias the sampling of the configuration space, leading the planner to behave similarly to the learned expert behavior. The approach is evaluated in experiments with real trajectories and compared with Inverse Reinforcement Learning algorithms that use RRT* as underlying planner.

The recently discovered $T_{cc}^+$ is evaluated as a $DD^*$ molecular structure in the $J^P=1^+$ sector. A coupled-channels calculation in charged basis, considering the $D^0D^{*\,+}$, $D^+D^{*\,0}$ and $D^{*\,0}D^{*\,+}$ channels, is done in the framework of a constituent quark model that successfully described other molecular candidates in the charmonium spectrum such as the $X(3872)$. The $T_{cc}^+$ is found as a $D^0D^{*\,+}$ molecule ($87\%$) with a binding energy of $387$ keV/c$^2$ and a width of $81$ keV, in agreement with the experimental measurements. The quark content of the state forces the inclusion of exchange diagrams to treat indistinguishable quarks between the $D$ mesons, which are found to be essential to bind the molecule. The $D^0D^0\pi^+$ line shape, scattering lengths and effective ranges of the molecule are also analyzed, which are found to be in agreement with the LHCb analysis. We search for further partners of the $T_{cc}^+$ in other charm and bottom sectors, finding different candidates. In particular, in the charm sector we find a shallow $J^P=1^+$ $D^+D^{*\,0}$ molecule ($83\%$), dubbed $T_{cc}^\prime$, just $1.8$ MeV above the $T_{cc}^+$ state. In the bottom sector, we find an isoscalar and an isovector $J^P=1^+$ bottom partners, as $BB^*$ molecules lying $21.9$ MeV/c$^2$ ($I=0$) and $10.5$ MeV/c$^2$ ($I=1$), respectively, below the $B^0B^{*\,+}$ threshold.

The last decade has seen a marked shift in how the internal structure of hadrons is understood. Modern experimental facilities, new theoretical techniques for the continuum bound-state problem and progress with lattice-regularised QCD have provided strong indications that soft quark+quark (diquark) correlations play a crucial role in hadron physics. For example, theory indicates that the appearance of such correlations is a necessary consequence of dynamical chiral symmetry breaking, viz. a corollary of emergent hadronic mass that is responsible for almost all visible mass in the universe; experiment has uncovered signals for such correlations in the flavour-separation of the proton's electromagnetic form factors; and phenomenology suggests that diquark correlations might be critical to the formation of exotic tetra- and penta-quark hadrons. A broad spectrum of such information is evaluated herein, with a view to consolidating the facts and therefrom moving toward a coherent, unified picture of hadron structure and the role that diquark correlations might play.

The low-lying bottomonium-like tetraquarks $b\bar{b}q\bar{q}$ $(q=u,\,d,\,s)$ with spin-parity $J^P=0^+$, $1^+$ and $2^+$, and isospin $I=0,\,1$ or $\frac{1}{2}$, are systematically investigated within the theoretical framework of real- and complex-scaling range of a chiral quark model, which has already been successfully applied in analysis of several multiquark systems. A complete four-body $S$-wave function, which includes meson-meson, diquark-antidiquark and K-type arrangements of quarks, along with all possible color configurations are considered. In the $b\bar{b}q\bar{q}$ $(q=u,\,d)$ tetraquark system, we found resonance states of $B^{(*)} \bar{B}^{(*)}$, $\Upsilon\omega$, $\Upsilon\rho$ and $\eta_b \rho$ nature with all possible $I(J^P)$ quantum numbers. Their masses are generally located in the energy range $11.0-11.3$ GeV and their widths are less than $10$ MeV. In addition, extremely narrow resonances, with two-meson strong decay widths less than $1.5$ MeV, are obtained in both $b\bar{b}u\bar{s}$ and $b\bar{b}s\bar{s}$ tetraquark systems. Particularly, four radial excitations of $\Upsilon K^*$ and $\eta_b K^*$ are found at $\sim11.1$ GeV in $J^P=0^+$, $1^+$ and $2^+$ channels of $b\bar{b}u\bar{s}$ system. One $\Upsilon(1S)\phi(2S)$ resonance state is obtained at $11.28$ GeV in the $J^P=1^+$ sector.

We recently reported new results on the $\gamma^{(*)} + N(940)\frac{1}{2}^+ \to \Delta(1700)\frac{3}{2}^{-}$ transition form factors using a symmetry-preserving treatment of a vector$\,\otimes\,$vector contact interaction (SCI) within a coupled formalism based on the Dyson-Schwinger, Bethe-Salpeter, and Faddeev equations. In this work, we extend our investigation to the $\gamma^{(*)} + N(940)\frac{1}{2}^+ \to N(1520)\frac{3}{2}^{-}$ transition. Our computed transition form factors show reasonable agreement with experimental data at large photon virtualities. However, deviations emerge at low $Q^2$, where experimental results exhibit a sharper variation than theoretical predictions. This discrepancy is expected, as these continuum QCD analyses account only for the quark-core of baryons, while low photon virtualities are dominated by meson cloud effects. We anticipate that these analytical predictions, based on the simplified SCI framework, will serve as a valuable benchmark for more refined studies and QCD-based truncations that incorporate quark angular momentum and the contributions of scalar and vector diquarks.

We employ all-atom well-tempered metadynamics simulations to study the mechanistic details of both the early stages of nucleation and crystal decomposition for the benchmark metal-organic framework ZIF-8. To do so, we developed and validated a force field that reliably models the modes of coordination bonds via a Morse potential functional form and employs cationic and anionic dummy atoms to capture coordination symmetry. We also explored a set of physically relevant collective variables and carefully selected an appropriate subset for our problem at hand. After a rapid increase of the Zn-N connectivity, we observe the evaporation of small clusters in favor of a few large clusters, that lead to the formation of an amorphous highly-connected aggregate. Zn(MIm)42- and Zn(MIm)3- complexes are observed, with lifetimes in the order of a few picoseconds, while larger structures, such as 4-, 5- and 6-membered rings, have substantially longer lifetimes of a few nanoseconds. The free ligands act as ``templating agents'' for the formation of the sodalite cages. ZIF-8 crystal decomposition results in the formation of a vitreous phase. Our findings contribute to a fundamental understanding of MOF's synthesis that paves the way to controlling synthesis products. Furthermore, our developed force field and methodology can be applied to model solution processes that require coordination bond reactivity for other ZIFs besides ZIF-8.