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Kun Cao

Publications and source records attributed to Kun Cao.

At least 19 recordsLinked to original sources
arXiv2026

DexForge: High-Fidelity Physics-Informed Dexterous Retargeting

Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reproduction of demonstrated motions and hand-object interactions across robot embodiments remains challenging under physical constraints. We present DexForge, a differentiable physics-grounded framework for converting human video demonstrations into high-fidelity robot trajectories. We reconstruct spherical-Gaussian object models and hand-object motion from visual observations, then build a differentiable simulator combining efficient Gaussian collision detection with existing differentiable dynamics. Based on this simulator, DexForge combines contact-aware kinematic retargeting with force-aware dynamics retargeting: robot-adapted stable contacts guide kinematic reference construction and subsequent gradient-based control refinement for precise physical motion reproduction. Experiments on 130 DexYCB and HOT3D demonstrations across seven dexterous hands show success-rate gains of approximately 35-53 percentage points over the baseline, with object position and orientation tracking errors on successful trajectories reduced by approximately 34-67% and 71-78%, respectively. Further experiments demonstrate open-loop transfer to MuJoCo and real-robot execution. Our project page is available at https://wmz1226.github.io/DexForge/

cs.RO↗
arXiv2026

From Local Whole-Body VLA Behaviors to Scene-Scale Aerial Manipulation

Vision-language-action (VLA) models enable task-conditioned interaction, but extending them to scene-scale aerial manipulation remains challenging due to costly whole-body demonstrations, latency-induced action-state misalignment, and cross-site behavior composition. We present a unified framework for synthetic policy training and scene-scale execution on articulated uncrewed aerial manipulators (UAMs). A scene-reconfigurable pipeline synthesizes task-conditioned, kinodynamically feasible trajectories and synchronized multiview observations for VLA training without physical-platform demonstrations. Measured-progress-aligned realization (MPAR) aligns asynchronously returned action chunks with measured execution progress and realizes them as continuous, dynamically feasible trajectories. A relational Scene Graph grounds language goals to object instances and feasible interaction regions, while topology-guided transfer connects local behaviors across sites. Local VLA skills achieve 39/60 successes (65.0%) in simulation under oracle target and feasible-handoff conditions. Under 500-ms added latency, with and without a transient command-update stall, MPAR reduces median takeover phase error by 0.212 s over nominal-time alignment. The complete system completes 21/50 simulated multi-site missions (42.0%) and is further validated on a physical articulated UAM.

cs.RO↗
arXiv2026

Whole-Body Aerial Grasping and Lifting via Partial Visual Observations

Aerial grasp-and-lift tasks require whole-body coordination across approach, acquisition, and lifting under partial target observations. Early approach failures can limit exposure to later task stages during training, while changing visibility complicates alignment and closure timing during execution. We present a recurrent teacher-student framework that learns a single policy in simulation to jointly command flight, arm motion, and gripper closure without an explicit task-phase input. A privileged teacher learns through reinforcement learning with a critical-state curriculum that exposes acquisition and lifting states before connecting them to normal approach trajectories. Its behavior is distilled into a recurrent visual student that replaces privileged target states with dual-view point clouds and proprioception, integrating observation history for closed-loop control. A dedicated closure objective supervises closure timing from sustained model-defined readiness sequences. Training and primary evaluation use a simulated acquisition-and-payload model with condition-triggered latching, virtual attachment, and wrench-based payload loading for short-distance lifting. Across 8,996 completed simulation episodes under this model, the frozen student achieves full-task success rates of 99.97%, 97.14%, and 95.84% under nominal, physics/control-randomized, and additional camera-randomized conditions, respectively. The nominal latch-count-weighted mean of per-seed 90th-percentile alignment errors at acquisition is 8.12 mm.

cs.RO↗
arXiv2026

Identifying the structure of La3Ni2O7 in the pressurized superconducting state

The crystal structure of La3Ni2O7 in its high-pressure superconducting state has been the subject of intense debate, with conflicting reports proposing orthorhombic (Amam or Fmmm) and tetragonal (I4/mmm) symmetries. Here, using high-pressure Raman spectroscopy down to 3 K, we resolve this controversy by tracking the structural evolution of La3Ni2O7 up to 32.7 GPa. Leveraging rigorous symmetry-based selection rules, we identify a single structural transition from the orthorhombic Amam phase to the Fmmm phase at ~14.5 GPa, signaled by a profound phonon renormalization. Crucially, the persistence of D2h symmetry across the transition rules out the tetragonal I4/mmm phase in the superconducting state in our measurements. The emergence of bulk superconductivity coincides precisely with this transition. Our results establish the orthorhombic Fmmm structure as the intrinsic host of superconductivity in La3Ni2O7 below 19.45 GPa, resolving a central structural controversy and providing a critical foundation for understanding the superconducting mechanism in bilayer nickelates.

cond-mat.supr-con↗
arXiv2026

VCAR: Training-Free 3DGS Segmentation via View Completeness and Axis-Aware Boundary Refinement

Semantic segmentation in 3D Gaussian Splatting (3DGS) is crucial for advancing 3D scene understanding. Existing methods predominantly rely on feature distillation, which incurs substantial per-scene training overhead and often yields blurred segmentation boundaries. We identify that these boundary artifacts are driven in part by insufficient viewpoint coverage and boundary overflow of anisotropic Gaussian primitives. To address these challenges, we propose VCAR, a training-free coarse-to-fine segmentation strategy based on View Completeness and Axis-aware Boundary Refinement. In the coarse stage, a visibility-based weighted multi-view voting scheme rapidly localizes the target. In the fine stage, an object-centric sphere derived from the coarse result generates supplementary viewpoints via Spherical Spiral Sampling (SSS), allowing multi-view voting on the augmented views to precisely refine object boundaries and suppress irrelevant 3D Gaussians. Moreover, we introduce Axis-aware Boundary Refinement (ABR) to mitigate artifacts from anisotropic primitives. By decomposing the projected 2D covariance into per-axis contributions, ABR identifies the dominant axis responsible for boundary leakage and applies targeted anisotropic compression exclusively along that axis. Extensive experiments on NVOS and LERF demonstrate that VCAR achieves state-of-the-art segmentation accuracy and efficiency without training. Our code is available at https://github.com/DDKK0526/VCAR.

cs.CV↗
arXiv2026

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces

Accurate and efficient modeling of magnetic potential energy surfaces remains challenging because spin-polarized first-principles calculations for diverse non-collinear spin-lattice configurations are computationally demanding. Here we introduce the Spin Tensor Equivariant Potential (STEP), a magnetic machine-learning interatomic potential that treats vector magnetic moments as continuous geometric degrees of freedom and embeds them in an equivariant representation. By coupling the central spin representation to its local spin-lattice environment through a Center-Environment Tensor Product, STEP introduces a physics-informed bias while preserving translational invariance and $\mathrm{SO}(3)$ equivariance and supporting feature-level time-reversal symmetrization. Learning-curve analysis on monolayer CrI$_3$ shows that STEP achieves pronounced data efficiency, with higher-order tensor channels and iterative center-environment couplings leading to steep learning curves for energy, force, and magnetic force errors. On public FeAl, CrN, and Fe benchmarks, STEP achieves competitive or improved accuracy compared with recent magnetic machine-learning potentials. Using a compact but representative CrI$_3$ dataset, STEP reproduces phonon dispersions and magnon spectra with high fidelity, capturing subtle anisotropic magnetic interactions. For Fe$_2$Mo$_3$O$_8$, STEP further provides a quantitative description of magnon--phonon hybridization and reproduces its characteristic magnon polaron dispersion. Finally, spin dynamics simulations driven by STEP yield Curie temperatures for monolayer CrI$_3$ and bcc Fe in good agreement with experiments. These results establish STEP as a physically informed, data-efficient, and scalable framework for modeling spin-lattice coupling, magnetic excitations, and finite-temperature magnetic behavior.

cond-mat.mtrl-sci↗
arXiv2026

FastTPS: An Optimized Method for LLM Token Phase for AI accelerators

The popularity of large language models (LLMs) escalates an ongoing demand for effective inference. However, due to the sequential processing of tokens during the token phase in decoder-only LLMs inference, the inherent low parallelism leads to reduced throughput and suboptimal utilization of the computing units on artificial intelligence (AI) accelerators, particularly when handling long-sequence inputs that impose significant memory overhead. Recently, many reported methods have been developed as potential solutions, since they emerge with numeric deviation. This paper presents FastTPS, a high performance and low-precision loss method for accelerating the token-phase in LLM inference on general AI accelerators which includes three key components: (1) AI accelerator-enabled reloading-free KV Cache concatenation which decreases memory access overhead as well as enables full fusion of Attention, (2) high-efficiency and high-accuracy 'RoPE' attention based on the tiling optimized FLAT, and (3) highly-fused MLP with fine-grain pipeline scheduling. Our results confirm that FastTPS significantly alleviates memory bottlenecks in the token phase, delivering a 6x speed improvement (compared to none-fusion) on an AMD Ryzen AI 300 series NPU with BF16 precision while sustaining 93% peak memory bandwidth utilization during Phi3-mini-4k-instruct inference.

cs.LG↗
arXiv2026

Spin correlations in La$_3$Ni$_2$O$_7$ thin films

The discovery of ambient-pressure superconductivity with $T_{c,\text{onset}} > 40$ K in La$_3$Ni$_2$O$_7$ (LNO) thin films grown on the SrLaAlO$_4$ (SLAO) substrate with compressive ($\varepsilon\approx-2\%$) epitaxial strain provides a unique platform for investigating the superconducting mechanism in nickelate superconductors. Here, we use resonant inelastic X-ray scattering (RIXS) to unveil the dispersive spin excitations in the LNO/SLAO thin film and establish the strain dependence of the electronic and spin excitations in LNO thin films with strain ranging from $\varepsilon\approx-2\%$ to $+1.9\%$. Compared with bulk LNO, LNO/SLAO exhibits similar $dd$ excitations and spin dynamics, but with a larger spin-excitation bandwidth, whereas tensile-strained LNO/SrTiO$_3$ exhibits a marked suppression of both the spin excitations and the Ni $3d_{z^2}$-derived $dd$ excitations. This evolution reflects a strain-tuned interlayer exchange interaction $J_z$ and Ni $3d_{z^2}$-O 2$p_z$ hybridization. Our results demonstrate how epitaxial strain modulates the interlayer magnetic coupling and are consistent with scenarios in which the interlayer antiferromagnetic superexchange interaction promotes interlayer pairing in bilayer nickelates.

cond-mat.supr-con↗
arXiv2026

Element-based Formation Control: a Unified Perspective from Continuum Mechanics

This paper establishes a unified element-based framework for formation control by introducing the concept of the deformation gradient from continuum mechanics. Unlike traditional methods that rely on geometric constraints defined on graph edges, we model the formation as a discrete elastic body composed of simplicial elements. By defining a generalized distortion energy based on the local deformation gradient tensor, we derive a family of distributed control laws that can enforce various geometric invariances, including translation, rotation, scaling, and affine transformations. The convergence properties and the features of the proposed controllers are analyzed in detail. Theoretically, we show that the proposed framework serves as a bridge between existing rigidity-based and Laplacian-based approaches. Specifically, we show that rigidity-based controllers are mathematically equivalent to minimizing specific projections of the deformation energy tensor. Furthermore, we establish a rigorous link between the proposed energy minimization and Laplacian-based formation control. Numerical simulations in 2D and 3D validate the effectiveness and the unified nature of the proposed framework.

eess.SY↗
arXiv2026

ContactGaussian-WM: Learning Physics-Grounded World Model from Videos

Developing world models that understand complex physical interactions is essential for advancing robotic planning and simulation.However, existing methods often struggle to accurately model the environment under conditions of data scarcity and complex contact-rich dynamic motion.To address these challenges, we propose ContactGaussian-WM, a differentiable physics-grounded rigid-body world model capable of learning intricate physical laws directly from sparse and contact-rich video sequences.Our framework consists of two core components: (1) a unified Gaussian representation for both visual appearance and collision geometry, and (2) an end-to-end differentiable learning framework that differentiates through a closed-form physics engine to infer physical properties from sparse visual observations.Extensive simulations and real-world evaluations demonstrate that ContactGaussian-WM outperforms state-of-the-art methods in learning complex scenarios, exhibiting robust generalization capabilities.Furthermore, we showcase the practical utility of our framework in downstream applications, including data synthesis and real-time MPC.

cs.RO↗
arXiv2026

Spin angular momentum transfer in the Einstein-de Haas effect

We investigate spin angular momentum transfer in the Einstein-de Haas effect within prototypical magnetic crystals, focusing on its partition between phonons and rigid-body rotation. Using the Eckart frame to decouple local vibrations (phonons) from rigid-body rotation, we demonstrate that spin angular momentum is simultaneously transferred into both phonons and rigid-body rotation in an asymmetric way: rigid-body rotation acquires the dominant share of angular momentum, while phonons absorb most of the resulting kinetic energy. This divergent transfer of angular momentum and energy identifies phonons as direct and indispensable participants in the Einstein-de Haas dynamics. Furthermore, we find that pseudo-dipolar anisotropy and Dzyaloshinskii-Moriya interaction exert distinct control over the angular momentum transfer. Stronger pseudo-dipolar anisotropy increases the total amount of transferred angular momentum, whereas stronger Dzyaloshinskii-Moriya interaction accelerates the transfer rate and increases the proportion of phonon angular momentum. Our work clarifies the microscopic picture of the Einstein-de Haas effect and enables targeted angular-momentum control in magneto-mechanical devices.

cond-mat.mes-hall↗
arXiv2025

NeRC: Neural Ranging Correction through Differentiable Moving Horizon Location Estimation

GNSS localization using everyday mobile devices is challenging in urban environments, as ranging errors caused by the complex propagation of satellite signals and low-quality onboard GNSS hardware are blamed for undermining positioning accuracy. Researchers have pinned their hopes on data-driven methods to regress such ranging errors from raw measurements. However, the grueling annotation of ranging errors impedes their pace. This paper presents a robust end-to-end Neural Ranging Correction (NeRC) framework, where localization-related metrics serve as the task objective for training the neural modules. Instead of seeking impractical ranging error labels, we train the neural network using ground-truth locations that are relatively easy to obtain. This functionality is supported by differentiable moving horizon location estimation (MHE) that handles a horizon of measurements for positioning and backpropagates the gradients for training. Even better, as a blessing of end-to-end learning, we propose a new training paradigm using Euclidean Distance Field (EDF) cost maps, which alleviates the demands on labeled locations. We evaluate the proposed NeRC on public benchmarks and our collected datasets, demonstrating its distinguished improvement in positioning accuracy. We also deploy NeRC on the edge to verify its real-time performance for mobile devices.

cs.LG↗
arXiv2025

Sliding multiferrocity in van der Waals layered CrI$_2$

Understanding magnetoelectric coupling in emerging van der Waals multiferroics is crucial for developing atomically thin spintronic devices. Here, we present a comprehensive first-principles investigation of magnetoelectric coupling in orthorhombic CrI$_2$. Monte Carlo simulations based on DFT-calculated magnetic exchange interactions suggest a proper-screw helimagnetic ground state with a Néel temperature consistent with experimental observations. A ferroelectric switching pathway driven by interlayer sliding is predicted, featuring a low switching energy barrier and out-of-plane ferroelectric polarization. To quantitatively characterize the magnetoelectric effect in orthorhombic CrI$_2$ and its microscopic origin, we evaluate the spin-driven polarization using the paramagnetic phase as a reference alongside the magnetoelectric tensor method. The extracted spin-driven polarization aligns along the $z$-axis, with its origin dominated by the exchange-striction mechanism. Although in-plane components of the total polarization in the bulk vanish due to global symmetry constraints, each CrI$_2$ single layer exhibits local electric polarization along the $x$ direction, arising from the generalized spin-current mechanism, which couples spin chirality to the electric polarization. As a result, we further predict that a proper-screw helimagnetic state may persist in monolayer CrI$_2$, with its charity reversable by switching the in-plane electric polarization through applying external electric field, providing another promising candidate for electrical control of two-dimensional multiferroics.

cond-mat.mtrl-sci↗
arXiv2025

Uniaxial stress tuning of interfacial thermal conductance in cubic BAs/4H-SiC heterostructures

Understanding interfacial thermal transport is essential for improving thermal management in high-speed power electronic devices, where the efficient removal of excess heat is a critical challenge. In this study, a machine learning interatomic potential with near first-principles accuracy was employed to investigate the interfacial thermal conductance (ITC) between [111]-oriented cubic boron arsenide (cBAs) and [0001]-oriented 4H silicon carbide (4H-SiC), as well as its dependence on uniaxial stress. Among all possible bonding configurations at the cBAs(111)/4H-SiC(0001) interface, the B-C bonded interface was identified as the most energetically favorable. Non-equilibrium molecular dynamics simulations revealed that, under ambient conditions (300 K and 0 GPa), the ITC of the B-C interface reaches 353 $\pm$ 6 MW m$^{-2}$ K$^{-1}$, and increases monotonically to 460 $\pm$ 3 MW m$^{-2}$ K$^{-1}$ under a uniaxial stress of 25 GPa perpendicular to the interface. For comparison, the As-C bonded interface exhibits a lower ITC, increasing from 233 $\pm$ 7 to 318 $\pm$ 6 MW m$^{-2}$ K$^{-1}$ over the same stress range. These results demonstrate that proper interfacial bonding and moderate uniaxial stress can significantly enhance thermal transport across the cBAs(111)/4H-SiC(0001) heterointerface, offering valuable insight for thermal design in next-generation power electronics.

cond-mat.mtrl-sci↗
arXiv2025

Unsupervised Anomaly Detection for Autonomous Robots via Mahalanobis SVDD with Audio-IMU Fusion

Reliable anomaly detection is essential for ensuring the safety of autonomous robots, particularly when conventional detection systems based on vision or LiDAR become unreliable in adverse or unpredictable conditions. In such scenarios, alternative sensing modalities are needed to provide timely and robust feedback. To this end, we explore the use of audio and inertial measurement unit (IMU) sensors to detect underlying anomalies in autonomous mobile robots, such as collisions and internal mechanical faults. Furthermore, to address the challenge of limited labeled anomaly data, we propose an unsupervised anomaly detection framework based on Mahalanobis Support Vector Data Description (M-SVDD). In contrast to conventional SVDD methods that rely on Euclidean distance and assume isotropic feature distributions, our approach employs the Mahalanobis distance to adaptively scale feature dimensions and capture inter-feature correlations, enabling more expressive decision boundaries. In addition, a reconstruction-based auxiliary branch is introduced to preserve feature diversity and prevent representation collapse, further enhancing the robustness of anomaly detection. Extensive experiments on a collected mobile robot dataset and four public datasets demonstrate the effectiveness of the proposed method, as shown in the video https://youtu.be/yh1tn6DDD4A. Code and dataset are available at https://github.com/jamesyang7/M-SVDD.

cs.RO↗
arXiv2025

Chiral magnon splitting in altermagnetic CrSb from first principles

Altermagnetism has been proposed as a new type of magnetism, simultaneously exhibiting compensated spin moments in real space and spin-split electronic bands in reciprocal space. Alternating chiral magnon splitting is considered a unique feature of altermagnets. In this work, utilizing linear spin wave theory (LSWT), which is based on a localized spin picture and itinerant time-dependent density functional perturbation theory (TD-DFPT), we investigate the spin fluctuation spectra of altermagnetic CrSb. Along the L-$Γ$-L$^{\prime}$ path, the LSWT provides a chiral magnon splitting of up to 9 meV, located at high excitation energies around 140 meV, which is identified to be primarily driven by the splitting of two long-range exchange interactions, with exchange paths along the body diagonal lines of the unit cell. On the other hand, the more realistic TD-DFPT obtains more significant splitting of $\sim$ 30 meV at maximum. However, the splitting is severely smeared out due to strong Landau damping from the Stoner continuum, which may make it difficult to observe experimentally, e.g. through inelastic neutron scattering. We further provide a brief discussion on the connection between the Stoner excitations and the chiral magnon splitting.

cond-mat.mtrl-sci↗
arXiv2025

HelmetPoser: A Helmet-Mounted IMU Dataset for Data-Driven Estimation of Human Head Motion in Diverse Conditions

Helmet-mounted wearable positioning systems are crucial for enhancing safety and facilitating coordination in industrial, construction, and emergency rescue environments. These systems, including LiDAR-Inertial Odometry (LIO) and Visual-Inertial Odometry (VIO), often face challenges in localization due to adverse environmental conditions such as dust, smoke, and limited visual features. To address these limitations, we propose a novel head-mounted Inertial Measurement Unit (IMU) dataset with ground truth, aimed at advancing data-driven IMU pose estimation. Our dataset captures human head motion patterns using a helmet-mounted system, with data from ten participants performing various activities. We explore the application of neural networks, specifically Long Short-Term Memory (LSTM) and Transformer networks, to correct IMU biases and improve localization accuracy. Additionally, we evaluate the performance of these methods across different IMU data window dimensions, motion patterns, and sensor types. We release a publicly available dataset, demonstrate the feasibility of advanced neural network approaches for helmet-based localization, and provide evaluation metrics to establish a baseline for future studies in this field. Data and code can be found at https://lqiutong.github.io/HelmetPoser.github.io/.

cs.RO↗
arXiv2025

Microscopic origin of magnetoferroelectricity in monolayer NiBr$_{2}$ and NiI$_{2}$

We investigate the magnetoelectric properties of the monolayer NiX$_{2}$ (X = Br, I) through first-principles calculations. Our calculations predict that the NiBr$_{2}$ monolayer exhibits a cycloidal magnetic ground state. For the NiI$_{2}$ monolayer, a proper-screw helical magnetic ground state with modulation vector \(\boldsymbol{Q} = (q, 0, 0)\) is adopted, approximated based on experimental observations. The electric polarization in NiBr$_{2}$ shows a linear dependence on the spin-orbit coupling strength \(λ_{\text{SOC}}\), which can be adequately described by the generalized Katsura-Nagaosa-Balatsky (gKNB) model, considering contributions from up to the third nearest-neighbor spin pairs. In contrast, the electric polarization in NiI$_{2}$ exhibits a distinct dependence on \(q\) and \(λ_{\text{SOC}}\), which cannot be fully explained by the gKNB mechanism alone. To address this, the \(p\)-\(d\) hybridization mechanism is extended to NiI$_{2}$ to explain the observed behavior. The respective contributions from the \(p\)-\(d\) hybridization and the gKNB mechanism in NiI$_{2}$ are then quantitatively evaluated. Overall, our work elucidates the microscopic mechanisms underlying multiferroicity in NiBr$_{2}$ and NiI$_{2}$ monolayers, with the conclusions readily applicable to their bulk forms.

cond-mat.mtrl-sci↗