arXiv subjects

Kejun Li

Publications and source records attributed to Kejun Li.

At least 19 recordsLinked to original sources

arXiv2026

The Split Janus-faced Sun: Magnetic Rhythm and Duality in the Solar Cycle

The solar cycle - most notably characterized by its sunspot activity patterns - serves as a cornerstone of heliospheric physics. This research uncovers a fundamental magnetic dichotomy in the Sun's full-disk field, identifying two functionally separate populations: the Strong-Field Group (SG) and Weak-Field Group (WG). The solar cycle exhibits a dual nature, much like Janus, with the SG and WG operating in opposing phases regardless of low or high latitudes. The SG-dominated cycle represents one facet of this duality and is visually prominent at the solar surface. It is well-established that this component synchronizes with the sunspot cycle at low latitudes but operates in anti-phase at high latitudes. In contrast, the WG-driven cycle acts as its hidden counterpart, functioning in opposition to the SG at both high and low latitudes -- a behavior that had not been identified until now. Influenced by these magnetic field groups, this dual nature permeates the entire solar atmosphere, revealing that the full-disk solar activity is globally modulated by the Janus cycle.

astro-ph.SR↗

arXiv2025

Chasing Stability: Humanoid Running via Control Lyapunov Function Guided Reinforcement Learning

Achieving highly dynamic behaviors on humanoid robots, such as running, requires controllers that are both robust and precise, and hence difficult to design. Classical control methods offer valuable insight into how such systems can stabilize themselves, but synthesizing real-time controllers for nonlinear and hybrid dynamics remains challenging. Recently, reinforcement learning (RL) has gained popularity for locomotion control due to its ability to handle these complex dynamics. In this work, we embed ideas from nonlinear control theory, specifically control Lyapunov functions (CLFs), along with optimized dynamic reference trajectories into the reinforcement learning training process to shape the reward. This approach, CLF-RL, eliminates the need to handcraft and tune heuristic reward terms, while simultaneously encouraging certifiable stability and providing meaningful intermediate rewards to guide learning. By grounding policy learning in dynamically feasible trajectories, we expand the robot's dynamic capabilities and enable running that includes both flight and single support phases. The resulting policy operates reliably on a treadmill and in outdoor environments, demonstrating robustness to disturbances applied to the torso and feet. Moreover, it achieves accurate global reference tracking utilizing only on-board sensors, making a critical step toward integrating these dynamic motions into a full autonomy stack.

cs.RO↗

arXiv2025

Hybrid Data-Driven Predictive Control for Robust and Reactive Exoskeleton Locomotion Synthesis

Robust bipedal locomotion in exoskeletons requires the ability to dynamically react to changes in the environment in real time. This paper introduces the hybrid data-driven predictive control (HDDPC) framework, an extension of the data-enabled predictive control, that addresses these challenges by simultaneously planning foot contact schedules and continuous domain trajectories. The proposed framework utilizes a Hankel matrix-based representation to model system dynamics, incorporating step-to-step (S2S) transitions to enhance adaptability in dynamic environments. By integrating contact scheduling with trajectory planning, the framework offers an efficient, unified solution for locomotion motion synthesis that enables robust and reactive walking through online replanning. We validate the approach on the Atalante exoskeleton, demonstrating improved robustness and adaptability.

cs.RO↗

arXiv2025

CLF-RL: Control Lyapunov Function Guided Reinforcement Learning

Reinforcement learning (RL) has shown promise in generating robust locomotion policies for bipedal robots, but often suffers from tedious reward design and sensitivity to poorly shaped objectives. In this work, we propose a structured reward shaping framework that leverages model-based trajectory generation and control Lyapunov functions (CLFs) to guide policy learning. We explore two model-based planners for generating reference trajectories: a reduced-order linear inverted pendulum (LIP) model for velocity-conditioned motion planning, and a precomputed gait library based on hybrid zero dynamics (HZD) using full-order dynamics. These planners define desired end-effector and joint trajectories, which are used to construct CLF-based rewards that penalize tracking error and encourage rapid convergence. This formulation provides meaningful intermediate rewards, and is straightforward to implement once a reference is available. Both the reference trajectories and CLF shaping are used only during training, resulting in a lightweight policy at deployment. We validate our method both in simulation and through extensive real-world experiments on a Unitree G1 robot. CLF-RL demonstrates significantly improved robustness relative to the baseline RL policy and better performance than a classic tracking reward RL formulation.

cs.RO↗

arXiv2025

Deep Spin Defects in Zinc Oxide for High-Fidelity Single-Shot Readout

Wide-bandgap oxides such as ZnO are favorable hosts for spin defect qubits due to their dilute nuclear spin background and potential for ultra-high purity. Yet, a deep-level defect qubit with robust optical and spin properties has not been identified in this material. Here, using first-principles calculations, we predict that the molybdenum-vacancy complex, Mo_Zn-V_O, exhibits the essential characteristics of an optically addressable spin qubit: a spin-triplet ground state, visible-range optical transitions with high quantum yield, and an unusually small Huang-Rhys factor (~5, compared to 10-30 in known ZnO defects). We further find long spin coherence times (T_2 ~ 4 ms) when both nuclear and impurity spin baths are considered, with paramagnetic impurities setting a threshold concentration of 0.035 ppm. Importantly, the combination of strong spin-orbit coupling and the absence of Jahn-Teller distortion supports spin-selective intersystem crossing and high-fidelity single-shot readout at elevated temperatures and across wide magnetic field ranges. By identifying ZnO as a host for deep-level defect qubits, our work points toward a pathway to scalable, integrable oxide-based quantum technologies and broadens the material foundation for solid-state quantum information science.

cond-mat.mtrl-sci↗

arXiv2024

Anomalous Hall effect from inter-superlattice scattering in a noncollinear antiferromagnet

Superlattice formation dictates the physical properties of many materials, including the nature of the ground state in magnetic materials. Chemical composition is commonly considered to be the primary determinant of superlattice identity, especially in intercalation compounds. Here, we find that, contrary to this conventional wisdom, kinetic control of superlattice growth leads to the coexistence of disparate domains within a compositionally "perfect" single crystal. We demonstrate that Cr$_{1/4}$TaS$_2$ is a bulk noncollinear antiferromagnet in which scattering between bulk and minority superlattice domains engenders complex magnetotransport below the N\'{e}el temperature, including an anomalous Hall effect. We characterize the magnetic phases in different domains, image their nanoscale morphology, and propose a mechanism for nucleation and growth. These results provide a blueprint for the deliberate engineering of macroscopic transport responses via microscopic patterning of magnetic exchange interactions in superlattice domains.

cond-mat.mtrl-sci↗

arXiv2024

Spin Dynamics in Hybrid Halide Perovskites -- Effect of Dynamical and Permanent Symmetry Breaking

The hybrid organic-inorganic halide perovskite (HOIP), for example MAPbBr3, exhibits extended spin lifetime and apparent spin lifetime anisotropy in experiments. The underlying mechanisms of these phenomena remain illusive. By utilizing our first-principles densitymatrix dynamics approach with quantum scatterings including electron-phonon and electronelectron interactions and self-consistent spinorbit coupling, we present temperature- and magnetic field-dependent spin lifetimes in hybrid perovskites, in agreement with experimental observations. For centrosymmetric hybrid perovskite MAPbBr3, the experimentally observed spin lifetime anisotropy is mainly attributed to the dynamical Rashba effect arising from the interaction between organic and inorganic components and the rotation of the organic cation. For noncentrosymmetric perovskite, such as MPSnBr3, we found persistent spin helix texture at the conduction band minimum, which significantly enhances the spin lifetime anisotropy. Our study provides theoretical insight to spin dynamics in HOIP and strategies for controlling and optimizing spin transport.

cond-mat.mtrl-sci↗

arXiv2024

Single nuclear spin detection and control in a van der Waals material

Optically active spin defects in solids are leading candidates for quantum sensing and quantum networking. Recently, single spin defects were discovered in hexagonal boron nitride (hBN), a layered van der Waals (vdW) material. Due to its two-dimensional structure, hBN allows spin defects to be positioned closer to target samples than in three-dimensional crystals, making it ideal for atomic-scale quantum sensing, including nuclear magnetic resonance (NMR) of single molecules. However, the chemical structures of these defects remain unknown, and detecting a single nuclear spin with an hBN spin defect has been elusive. In this study, we created single spin defects in hBN using $^{13}$C ion implantation and identified three distinct defect types based on hyperfine interactions. We observed both S=1 and S=1/2 spin states within a single hBN spin defect. We demonstrated atomic-scale NMR and coherent control of individual nuclear spins in a vdW material, with a $\pi$-gate fidelity up to 99.75% at room temperature. By comparing experimental results with density-functional theory calculations, we propose chemical structures for these spin defects. Our work advances the understanding of single spin defects in hBN and provides a pathway to enhance quantum sensing using hBN spin defects with nuclear spins as quantum memories.

quant-ph↗

arXiv2024

Preferential Multi-Objective Bayesian Optimization

Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple conflicting objectives, existing work in PBO assumes that preferences can be encoded by a single objective function. For example, in robotic assistive devices, technicians often attempt to maximize user comfort while simultaneously minimizing mechanical energy consumption for longer battery life. Similarly, in autonomous driving policy design, decision-makers wish to understand the trade-offs between multiple safety and performance attributes before committing to a policy. To address this gap, we propose the first framework for PBO with multiple objectives. Within this framework, we present dueling scalarized Thompson sampling (DSTS), a multi-objective generalization of the popular dueling Thompson algorithm, which may be of interest beyond the PBO setting. We evaluate DSTS across four synthetic test functions and two simulated exoskeleton personalization and driving policy design tasks, showing that it outperforms several benchmarks. Finally, we prove that DSTS is asymptotically consistent. As a direct consequence, this result provides, to our knowledge, the first convergence guarantee for dueling Thompson sampling in the PBO setting.

cs.LG↗

arXiv2024

Dynamic Walking on Highly Underactuated Point Foot Humanoids: Closing the Loop between HZD and HLIP

Realizing bipedal locomotion on humanoid robots with point feet is especially challenging due to their highly underactuated nature, high degrees of freedom, and hybrid dynamics resulting from impacts. With the goal of addressing this challenging problem, this paper develops a control framework for realizing dynamic locomotion and implements it on a novel point foot humanoid: ADAM. To this end, we close the loop between Hybrid Zero Dynamics (HZD) and Hybrid linear inverted pendulum (HLIP) based step length regulation. To leverage the full-order hybrid dynamics of the robot, walking gaits are first generated offline by utilizing HZD. These trajectories are stabilized online through the use of a HLIP based regulator. Finally, the planned trajectories are mapped into the full-order system using a task space controller incorporating inverse kinematics. The proposed method is verified through numerical simulations and hardware experiments on the humanoid robot ADAM marking the first humanoid point foot walking. Moreover, we experimentally demonstrate the robustness of the realized walking via the ability to track a desired reference speed, robustness to pushes, and locomotion on uneven terrain.

cs.RO↗

arXiv2024

Excited-State Dynamics and Optically Detected Magnetic Resonance of Solid-State Spin Defects from First Principles

Optically detected magnetic resonance (ODMR) is an efficient and reliable method that enables initialization and readout of spin states through spin-photon interface. In general, high quantum efficiency and large spin-dependent photoluminescence contrast are desirable for reliable quantum information readout. However, reliable prediction of the ODMR contrast from first-principles requires accurate description of complex spin polarization mechanisms of spin defects. These mechanisms often include multiple radiative and nonradiative processes in particular intersystem crossing (ISC)among multiple excited electronic states. In this work we present our implementation of the first-principles ODMR contrast, by solving kinetic master equation with calculated rates from \textit{ab initio} electronic structure methods then benchmark the implementation on the case of the negatively-charged nitrogen vacancy center in diamond. We show the importance of correct description of multi-reference electronic states and pseudo Jahn-Teller effect for quantitatively, even qualitatively correct prediction of spin-orbit coupling (SOC) and the rate of ISC. We present the complete calculation of SOC for different ISC processes that align with both group theory and experimental observations. Moreover, we provide a comprehensive picture of excitation and relaxation dynamics, including previously unexplored internal conversion processes. We show good agreement between our first-principles calculations and the experimental ODMR contrast under magnetic field. We then demonstrate reliable predictions of magnetic field direction, pump power, and microwave frequency dependency, as important parameters for ODMR experiments. Our work provides a predictive computational platform for spin polarization and optical readout of solid-state quantum defects from first principles.

cond-mat.mtrl-sci↗

arXiv2024

Data-Driven Predictive Control for Robust Exoskeleton Locomotion

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

cs.RO↗

arXiv2023

Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness -- the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.

cs.RO↗

arXiv2023

Comparative Electronic Structures of the Chiral Helimagnets Cr1/3NbS2 and Cr1/3TaS2

Magnetic materials with noncollinear spin textures are promising for spintronic applications. To realize practical devices, control over the length and energy scales of such spin textures is imperative. The chiral helimagnets Cr1/3NbS2 and Cr1/3TaS2 exhibit analogous magnetic phase diagrams with different real-space periodicities and field dependence, positioning them as model systems for studying the relative strengths of the microscopic mechanisms giving rise to exotic spin textures. Here, we carry out a comparative study of the electronic structures of Cr1/3NbS2 and Cr1/3TaS2 using angle-resolved photoemission spectroscopy and density functional theory. We show that bands in Cr1/3TaS2 are more dispersive than their counterparts in Cr1/3NbS2 and connect this result to bonding and orbital overlap in these materials. We also unambiguously distinguish exchange splitting from surface termination effects by studying the dependence of their photoemission spectra on polarization, temperature, and beam size. We find strong evidence that hybridization between intercalant and host lattice electronic states mediates the magnetic exchange interactions in these materials, suggesting that band engineering is a route toward tuning their spin textures. Overall, these results underscore how the modular nature of intercalated transition metal dichalcogenides translates variation in composition and electronic structure to complex magnetism.

cond-mat.mtrl-sci↗

arXiv2023

Effect of Environmental Screening and Strain on Optoelectronic Properties of Two-Dimensional Quantum Defects

Point defects in hexagonal boron nitride (hBN) are promising candidates as single-photon emitters (SPEs) in nanophotonics and quantum information applications. The precise control of SPEs requires in-depth understanding of their optoelectronic properties. However, how the surrounding environment of host materials, including number of layers, substrates, and strain, influences SPEs has not been fully understood. In this work, we study the dielectric screening effect due to the number of layers and substrates, and the strain effect on the optical properties of carbon dimer and nitrogen vacancy defects in hBN from first-principles many-body perturbation theory. We report that the environmental screening causes lowering of the GW gap and exciton binding energy, leading to nearly constant optical excitation energy and exciton radiative lifetime. We explain the results with an analytical model starting from the BSE Hamiltonian with Wannier basis. We also show that optical properties of quantum defects are largely tunable by strain with highly anisotropic response, in good agreement with experimental measurements. Our work clarifies the effect of environmental screening and strain on optoelectronic properties of quantum defects in two-dimensional insulators, facilitating future applications of SPEs and spin qubits in low-dimensional systems.

cond-mat.mtrl-sci↗

arXiv2022

How Spin Relaxes and Dephases in Bulk Halide Perovskites

Spintronics in halide perovskites has drawn significant attention in recent years, due to highly tunable spin-orbit fields and intriguing interplay with lattice symmetry. Spin lifetime -- a key parameter that determines the applicability of materials for spintronics and spin-based quantum information applications -- has been extensively measured in halide perovskites, but not yet assessed from first-principles calculations. Here, we leverage our recently-developed \emph{ab initio} density-matrix dynamics framework to compute the spin relaxation time ($T_{1}$) and ensemble spin dephasing time ($T_{2}^{*}$) in a prototype halide perovskite, namely CsPbBr$_{3}$ with self-consistent spin-orbit coupling (SOC) and quantum descriptions of the electron scattering processes. We also implement the Land\'e $g$-factor for solids from first principles and take it into account in our dynamics, which is required to accurately capture spin dephasing at external magnetic fields. We thereby predict intrinsic spin lifetimes as an upper bound for experiments, identify the dominant spin relaxation pathways, and evaluate the dependence on temperature, external fields, carrier density,and impurities. Importantly, we find that the Fr{\"o}hlich interaction that dominates carrier relaxation contributes negligibly to spin relaxation, consistent with the spin-conserving nature of this interaction. We investigated the effect of spin-orbit field with inversion asymmetry on spin lifetime, and we demonstrated from our calculation, persistent spin helix can enhance spin lifetime when the spin-split is large, but it can not be realized by Rashba SOC. Our theoretical approach may lead to new strategies to optimize spin and carrier transport properties in spintronics and quantum information applications.

cond-mat.mtrl-sci↗

arXiv2022

POLAR: Preference Optimization and Learning Algorithms for Robotics

Parameter tuning for robotic systems is a time-consuming and challenging task that often relies on domain expertise of the human operator. Moreover, existing learning methods are not well suited for parameter tuning for many reasons including: the absence of a clear numerical metric for `good robotic behavior'; limited data due to the reliance on real-world experimental data; and the large search space of parameter combinations. In this work, we present an open-source MATLAB Preference Optimization and Learning Algorithms for Robotics toolbox (POLAR) for systematically exploring high-dimensional parameter spaces using human-in-the-loop preference-based learning. This aim of this toolbox is to systematically and efficiently accomplish one of two objectives: 1) to optimize robotic behaviors for human operator preference; 2) to learn the operator's underlying preference landscape to better understand the relationship between adjustable parameters and operator preference. The POLAR toolbox achieves these objectives using only subjective feedback mechanisms (pairwise preferences, coactive feedback, and ordinal labels) to infer a Bayesian posterior over the underlying reward function dictating the user's preferences. We demonstrate the performance of the toolbox in simulation and present various applications of human-in-the-loop preference-based learning.

cs.RO↗

arXiv2022

Nuclear spin polarization and control in a van der Waals material

Van der Waals layered materials are a focus of materials research as they support strong quantum effects and can easily form heterostructures. Electron spins in van der Waals materials played crucial roles in many recent breakthroughs, including topological insulators, two-dimensional (2D) magnets, and spin liquids. However, nuclear spins in van der Waals materials remain an unexplored quantum resource. Here we report the first demonstration of optical polarization and coherent control of nuclear spins in a van der Waals material at room temperature. We use negatively-charged boron vacancy ($V_B^-$) spin defects in hexagonal boron nitride to polarize nearby nitrogen nuclear spins. Remarkably, we observe the Rabi frequency of nuclear spins at the excited-state level anti-crossing of $V_B^-$ defects to be 350 times larger than that of an isolated nucleus, and demonstrate fast coherent control of nuclear spins. We also detect strong electron-mediated nuclear-nuclear spin coupling that is 5 orders of magnitude larger than the direct nuclear spin dipolar coupling, enabling multi-qubit operations. Nitrogen nuclear spins in a triangle lattice will be suitable for large-scale quantum simulation. Our work opens a new frontier with nuclear spins in van der Waals materials for quantum information science and technology.

quant-ph↗