arXiv subjects

Kun Cao

Publications and source records attributed to Kun Cao.

At least 37 records · Page 2Linked to original sources

arXiv2024

Spin density wave in the bilayered nickelate La$_3$Ni$_2$O$_{7-δ}$ at ambient pressure

The recent discovery of high-temperature superconductivity in high-pressurized La$_3$Ni$_2$O$_{7-δ}$ has garnered significant attention. Using density functional theory, we investigate the magnetic properties of La$_3$Ni$_2$O$_{7-δ}$ at ambient pressure. Our calculations suggest that with $δ=0$, the double spin stripe phase is favored as the magnetic ground state. Oxygen vacancies may effectively turn nearest Ni spins into \textit{charge} sites. Consequently, with moderate $δ$ values, our theoretical magnetic ground state exhibits characteristics of both double spin stripe and spin-charge stripe configurations, providing a natural explanation to reconcile the seemingly contradictory experimental findings that suggest both the configurations as candidates for the spin-density-wave phase. With higher $δ$ values, we anticipate the ground state to become a spin-glass-like noncollinear magnetic phase with only short-range order. The oxygen vacancies are expected to significantly impact the magnetic excitations and the transition temperatures $T_{SDW}$. Notably, the magnetic ordering also induces concomitant charge ordering and orbital ordering, driven by spin-lattice coupling under the low symmetry magnetic order. We further offer a plausible explanation for the experimental observations that the measured $T_{SDW}$ appears insensitive to the variation of samples and the lack of direct evidence for long-range magnetic ordering.

cond-mat.mtrl-sci↗

arXiv2024

UA-MPC: Uncertainty-Aware Model Predictive Control for Motorized LiDAR Odometry

Accurate and comprehensive 3D sensing using LiDAR systems is crucial for various applications in photogrammetry and robotics, including facility inspection, Building Information Modeling (BIM), and robot navigation. Motorized LiDAR systems can expand the Field of View (FoV) without adding multiple scanners, but existing motorized LiDAR systems often rely on constant-speed motor control, leading to suboptimal performance in complex environments. To address this, we propose UA-MPC, an uncertainty-aware motor control strategy that balances scanning accuracy and efficiency. By predicting discrete observabilities of LiDAR Odometry (LO) through ray tracing and modeling their distribution with a surrogate function, UA-MPC efficiently optimizes motor speed control according to different scenes. Additionally, we develop a ROS-based realistic simulation environment for motorized LiDAR systems, enabling the evaluation of control strategies across diverse scenarios. Extensive experiments, conducted on both simulated and real-world scenarios, demonstrate that our method significantly improves odometry accuracy while preserving the scanning efficiency of motorized LiDAR systems. Specifically, it achieves over a 60\% reduction in positioning error with less than a 2\% decrease in efficiency compared to constant-speed control, offering a smarter and more effective solution for active 3D sensing tasks. The simulation environment for control motorized LiDAR is open-sourced at: \url{https://github.com/kafeiyin00/UA-MPC.git}.

cs.RO↗

arXiv2024

Exploring the energy landscape of aluminas through machine learning interatomic potential

Aluminum oxide (alumina, Al$_2$O$_3$) exists in various structures and has broad industrial applications. While the crystal structure of $α$-Al$_2$O$_3$ is well-established, those of transitional aluminas remain highly debated. In this study, we propose a universal machine learning interatomic potential (MLIP) for aluminas, trained using the neuroevolution potential (NEP) approach. The dataset is constructed through iterative training and farthest point sampling, ensuring the generation of the most representative configurations for an exhaustive sampling of the potential energy surface. The accuracy and generality of the potential are validated through simulations under a wide range of conditions, including high temperatures and pressures. A phase diagram is presented that includes both transitional aluminas and $α$-Al$_2$O$_3$ based on the NEP. We also successfully extrapolate the phase diagram of aluminas under extreme conditions ([0, 4000] K and [0, 200] GPa ranges of temperature and pressure, respectively), while maintaining high accuracy in describing their properties under more moderate conditions. Furthermore, combined with our developed structure search workflow, the NEP provides an evaluation of existing $γ$-Al$_2$O$_3$ structure models. The NEP developed in this work enables highly accurate dynamic simulations of various aluminas on larger scales and longer timescales, while also offering new insights into the study of transitional aluminas structures.

cond-mat.mtrl-sci↗

arXiv2024

Tuning magnetic anisotropy in Fe$_{5}$GeTe$_{2}$ monolayer through doping and strain

Controlling magnetic anisotropy energy (MAE) in two-dimensional (2D) ferromagnetic materials is crucial for designing novel spintronic devices. Using first-principles calculations, we systematically investigate the magnetic properties of monolayer Fe$_5$GeTe$_2$ (F5GT) under two scenarios: (I) Co and Ni doping, and (II) compressive and tensile strains. Our results show that the F5GT monolayer exhibits a weak in-plane MAE, which can be significantly enhanced by Co doping. Additionally, a $1\%$ compressive strain switches the magnetic easy axis from in-plane to out-of-plane, while $4\%$ compressive strain can further enhance the out-of-plane MAE. Spin-orbit coupling (SOC) matrix analysis reveals that the enhancement of in-plane MAE in Co-doped F5GT (Co-F5GT) arises from changes in $ \left\langle {\it p}_{x} \left\vert L_z\right\vert {\it p}_{y} \right\rangle $ of Te and $ \left\langle {\it d}_{xy} \left\vert L_z\right\vert {\it d}_{x^2+y^2} \right\rangle $ of Fe(2) and Fe(3). The effect of compressive strain is primarily attributed to a substantial increase in the positive contribution from $ \left\langle {\it d}_{xy} \left\vert L_z\right\vert {\it d}_{x^2+y^2} \right\rangle $ of Fe(1).

cond-mat.mtrl-sci↗

arXiv2024

Distance-based Multiple Non-cooperative Ground Target Encirclement for Complex Environments

This paper proposes a comprehensive strategy for complex multi-target-multi-drone encirclement in an obstacle-rich and GPS-denied environment, motivated by practical scenarios such as pursuing vehicles or humans in urban canyons. The drones have omnidirectional range sensors that can robustly detect ground targets and obtain noisy relative distances. After each drone task is assigned, a novel distance-based target state estimator (DTSE) is proposed by estimating the measurement output noise variance and utilizing the Kalman filter. By integrating anti-synchronization techniques and pseudo-force functions, an acceleration controller enables two tasking drones to cooperatively encircle a target from opposing positions while navigating obstacles. The algorithms effectiveness for the discrete-time double-integrator system is established theoretically, particularly regarding observability. Moreover, the versatility of the algorithm is showcased in aerial-to-ground scenarios, supported by compelling simulation results. Experimental validation demonstrates the effectiveness of the proposed approach.

cs.RO↗

arXiv2024

Coincidence detection probability of $(γ, 2e)$ photoemission measurement

In the study of strongly correlated electrons, one of the challenging core tasks is to develop the potential techniques for direct detection of the many-body correlations of strongly correlated electrons. The $(γ, 2e)$ photoemission technique has been developed to investigate the two-body correlations of the target correlated electrons. In this article, we will focus on this technique for the correlated electrons near the Fermi energy in condensed matter. The coincidence detection probability of the two emitted electrons in the $(γ, 2e)$ photoemission measurement is shown to be relevant to a two-body Bethe-Salpeter wave function, which describes the dynamical two-body correlations of the target correlated electrons near the Fermi energy. As the coincidence detection probability involves an electron-electron interaction matrix element, the arbitrary momentum and/or energy transfer due to this electron-electron interaction makes the $(γ, 2e)$ photoemission technique fail to reveal the inner-pair structures of the two-body Bethe-Salpeter wave function. However, the center-of-mass momentum and energy of the two-body Bethe-Salpeter wave function can be distinctly resolved. Thus, the $(γ, 2e)$ photoemission technique can provide the center-of-mass physics of the two-body correlations of the target correlated electrons. It will be one potential technique to study the center-of-mass physics of the Cooper pairs in superconductor.

cond-mat.supr-con↗

arXiv2024

Towards End-to-End GPS Localization with Neural Pseudorange Correction

The pseudorange error is one of the root causes of localization inaccuracy in GPS. Previous data-driven methods regress and eliminate pseudorange errors using handcrafted intermediate labels. Unlike them, we propose an end-to-end GPS localization framework, E2E-PrNet, to train a neural network for pseudorange correction (PrNet) directly using the final task loss calculated with the ground truth of GPS receiver states. The gradients of the loss with respect to learnable parameters are backpropagated through a Differentiable Nonlinear Least Squares (DNLS) optimizer to PrNet. The feasibility of fusing the data-driven neural network and the model-based DNLS module is verified with GPS data collected by Android phones, showing that E2E-PrNet outperforms the baseline weighted least squares method and the state-of-the-art end-to-end data-driven approach. Finally, we discuss the explainability of E2E-PrNet.

cs.LG↗

arXiv2024

A Differential Dynamic Programming Framework for Inverse Reinforcement Learning

A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and constraints from demonstrations. Different from existing work, where DDP was used for the inner forward problem with inequality constraints, our proposed framework uses it for efficient computation of the gradient required in the outer inverse problem with equality and inequality constraints. The equivalence between the proposed method and existing methods based on Pontryagin's Maximum Principle (PMP) is established. More importantly, using this DDP-based IRL with an open-loop loss function, a closed-loop IRL framework is presented. In this framework, a loss function is proposed to capture the closed-loop nature of demonstrations. It is shown to be better than the commonly used open-loop loss function. We show that the closed-loop IRL framework reduces to a constrained inverse optimal control problem under certain assumptions. Under these assumptions and a rank condition, it is proven that the learning parameters can be recovered from the demonstration data. The proposed framework is extensively evaluated through four numerical robot examples and one real-world quadrotor system. The experiments validate the theoretical results and illustrate the practical relevance of the approach.

cs.RO↗

arXiv2024

OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains, or lack underlying real academic graphs. In this paper, we present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated benchmark based on the Open Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines, and 120+ experimental results to date. We propose new data annotation strategies for certain tasks and offer a suite of data pre-processing codes, algorithm implementations, and standardized evaluation protocols to facilitate academic graph mining. Extensive experiments reveal that even advanced algorithms like large language models (LLMs) encounter difficulties in addressing key challenges in certain tasks, such as paper source tracing and scholar profiling. We also introduce the Open Academic Graph Challenge (OAG-Challenge) to encourage community input and sharing. We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field. OAG-Bench is accessible at https://www.aminer.cn/data/.

cs.DL↗

arXiv2024

Neutron Scattering Studies on the High-$T_c$ Superconductor La$_3$Ni$_2$O$_{7-δ}$ at Ambient Pressure

After several decades of studies of high-temperature superconductivity, there is no compelling theory for the mechanism yet; however, the spin fluctuations have been widely believed to play a crucial role in forming the superconducting Cooper pairs. The recent discovery of high-temperature superconductivity near 80 K in the bilayer nickelate La$_3$Ni$_2$O$_7$ under pressure provides a new platform to elucidate the origins of high-temperature superconductivity. We perform elastic and inelastic neutron scattering studies on a polycrystalline sample of La$_3$Ni$_2$O$_{7-δ}$ at ambient pressure. No magnetic order can be identified down to 10 K. The absence of long-range magnetic order in neutron diffraction measurements may be ascribed to the smallness of the magnetic moment. However, we observe a weak flat spin-fluctuation signal at $\sim$ 45 meV in the inelastic scattering spectra. The observed spin excitations could be interpreted as a result of strong interlayer and weak intralayer magnetic couplings for stripe-type antiferromagnetic orders. Our results provide crucial information on the spin dynamics and are thus important for understanding the superconductivity in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗

arXiv2024

HCTO: Optimality-Aware LiDAR Inertial Odometry with Hybrid Continuous Time Optimization for Compact Wearable Mapping System

Compact wearable mapping system (WMS) has gained significant attention due to their convenience in various applications. Specifically, it provides an efficient way to collect prior maps for 3D structure inspection and robot-based "last-mile delivery" in complex environments. However, vibrations in human motion and the uneven distribution of point cloud features in complex environments often lead to rapid drift, which is a prevalent issue when applying existing LiDAR Inertial Odometry (LIO) methods on low-cost WMS. To address these limitations, we propose a novel LIO for WMSs based on Hybrid Continuous Time Optimization (HCTO) considering the optimality of Lidar correspondences. First, HCTO recognizes patterns in human motion (high-frequency part, low-frequency part, and constant velocity part) by analyzing raw IMU measurements. Second, HCTO constructs hybrid IMU factors according to different motion states, which enables robust and accurate estimation against vibration-induced noise in the IMU measurements. Third, the best point correspondences are selected using optimal design to achieve real-time performance and better odometry accuracy. We conduct experiments on head-mounted WMS datasets to evaluate the performance of our system, demonstrating significant advantages over state-of-the-art methods. Video recordings of experiments can be found on the project page of HCTO: \href{https://github.com/kafeiyin00/HCTO}{https://github.com/kafeiyin00/HCTO}.

cs.RO↗

arXiv2024

PST-Bench: Tracing and Benchmarking the Source of Publications

Tracing the source of research papers is a fundamental yet challenging task for researchers. The billion-scale citation relations between papers hinder researchers from understanding the evolution of science efficiently. To date, there is still a lack of an accurate and scalable dataset constructed by professional researchers to identify the direct source of their studied papers, based on which automatic algorithms can be developed to expand the evolutionary knowledge of science. In this paper, we study the problem of paper source tracing (PST) and construct a high-quality and ever-increasing dataset PST-Bench in computer science. Based on PST-Bench, we reveal several intriguing discoveries, such as the differing evolution patterns across various topics. An exploration of various methods underscores the hardness of PST-Bench, pinpointing potential directions on this topic. The dataset and codes have been available at https://github.com/THUDM/paper-source-trace.

cs.DL↗

arXiv2023

Renormalization formalism for superconducting phase transition with inner-Cooper-pair dynamics

As charge carrier of the macroscopic superconductivity, the Cooper pair is a composite particle of two paired electrons, which has both center-of-mass and inner-pair degrees of freedom. In most cases, these two different degrees of freedom can be well described by the macroscopic Ginzburg-Landau theory and the microscopic Bardeen-Cooper-Schrieffer (BCS) theory, respectively. Near the superconducting phase transition where the Cooper pair is fragile and unstable because of the small binding energy, there are non-trivial couplings between these two different degrees of freedom due to such as finite energy and/or momentum transfer. The non-trivial couplings make the original derivation of the Ginzburg-Landau theory from the BCS theory fail in principle as where these two different degrees of freedom should not be decoupled. In this article, we will present a renormalization formalism for an extended Ginzburg-Landau action for the superconducting phase transition where there is finite energy transfer between the center-of-mass and the inner-pair degrees of freedom of Cooper pairs. This renormalization formalism will provide a theoretical tool to study the unusual dynamical effects of the inner-pair time-retarded physics on the superconducting phase transition.

cond-mat.supr-con↗

arXiv2023

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code.

cs.RO↗

arXiv2023

Path Planning for Multiple Tethered Robots Using Topological Braids

Path planning for multiple tethered robots is a challenging problem due to the complex interactions among the cables and the possibility of severe entanglements. Previous works on this problem either consider idealistic cable models or provide no guarantee for entanglement-free paths. In this work, we present a new approach to address this problem using the theory of braids. By establishing a topological equivalence between the physical cables and the space-time trajectories of the robots, and identifying particular braid patterns that emerge from the entangled trajectories, we obtain the key finding that all complex entanglements stem from a finite number of interaction patterns between 2 or 3 robots. Hence, non-entanglement can be guaranteed by avoiding these interaction patterns in the trajectories of the robots. Based on this finding, we present a graph search algorithm using the permutation grid to efficiently search for a feasible topology of paths and reject braid patterns that result in an entanglement. We demonstrate that the proposed algorithm can achieve 100% goal-reaching capability without entanglement for up to 10 drones with a slack cable model in a high-fidelity simulation platform. The practicality of the proposed approach is verified using three small tethered UAVs in indoor flight experiments.

cs.RO↗

arXiv2023

NEPTUNE: Nonentangling Trajectory Planning for Multiple Tethered Unmanned Vehicles

Despite recent progress on trajectory planning of multiple robots and path planning of a single tethered robot, planning of multiple tethered robots to reach their individual targets without entanglements remains a challenging problem. In this paper, we present a complete approach to address this problem. Firstly, we propose a multi-robot tether-aware representation of homotopy, using which we can efficiently evaluate the feasibility and safety of a potential path in terms of (1) the cable length required to reach a target following the path, and (2) the risk of entanglements with the cables of other robots. Then, the proposed representation is applied in a decentralized and online planning framework that includes a graph-based kinodynamic trajectory finder and an optimization-based trajectory refinement, to generate entanglement-free, collision-free and dynamically feasible trajectories. The efficiency of the proposed homotopy representation is compared against existing single and multiple tethered robot planning approaches. Simulations with up to 8 UAVs show the effectiveness of the approach in entanglement prevention and its real-time capabilities. Flight experiments using 3 tethered UAVs verify the practicality of the presented approach.

cs.RO↗

arXiv2023

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than $10\times$ speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation.

cs.RO↗

arXiv2023

DoubleBee: A Hybrid Aerial-Ground Robot with Two Active Wheels

We present the dynamic model and control of DoubleBee, a novel hybrid aerial-ground vehicle consisting of two propellers mounted on tilting servo motors and two motor-driven wheels. DoubleBee exploits the high energy efficiency of a bicopter configuration in aerial mode, and enjoys the low power consumption of a two-wheel self-balancing robot on the ground. Furthermore, the propeller thrusts act as additional control inputs on the ground, enabling a novel decoupled control scheme where the attitude of the robot is controlled using thrusts and the translational motion is realized using wheels. A prototype of DoubleBee is constructed using commercially available components. The power efficiency and the control performance of the robot are verified through comprehensive experiments. Challenging tasks in indoor and outdoor environments demonstrate the capability of DoubleBee to traverse unstructured environments, fly over and move under barriers, and climb steep and rough terrains.

cs.RO↗