Humans are remarkably efficient at learning tasks from demonstrations, but today’s imitation learning methods for robot manipulation often require hundreds or thousands of demonstrations per task. We investigated two fundamental priors for improving learning efficiency: decomposing manipulation trajectories into sequential alignment and interaction phases and retrieval-based generalization. Through

Physical scaling laws predict that miniaturizing robotic mechanisms should enable exceptional robot performance in metrics such as speed and precision. Although these scaling laws have been explored in a variety of microsystems, the benefits and limitations of downscaling three-dimensional (3D) robotic mechanisms have yet to be assessed because of limitations in microscale 3D manufacturing. In this

To improve the chassis performance of wheeled engineering vehicles, this paper proposes a control strategy that combines tire trajectory inner road profile estimation (TTIRPE) and active suspension preview control (ASPC). The highly accurate and timely TTIRPE method consists of two main components: the generation of a high-precision map, the generation and filtering of the road profile within the wheel–ground

Sparse array ultrasound imaging offers a cost-effective and scalable solution for nondestructive testing (NDT). However, the reduced number of transceiver elements typically compromises resolution and contrast. To address this limitation, this study proposes a novel nonlinear apodization method, null subtraction imaging (NSI), integrated with the total focusing method (TFM-NSI). This approach effectively

Metal fatigue cracks under cyclic loading conditions cannot be ignored. Ultrasound-induced thermography has been emerged as an effective technique for crack detection, however, single-frequency ultrasound excitation often produces weak thermal signals, especially for small or shallow cracks, limiting identification accuracy. To overcome these challenges, Multimodal Algorithm-enhanced Chirped Ultrasound-Induced

The asymmetric transformation elasticity provides a promising method for controlling elastic waves. However, it requires elastic materials capable of supporting asymmetric stresses, which are not admissible within the linearized Cauchy elasticity under small deformations. In contrast, asymmetric stress tensors naturally arise in micropolar continuum theory, yet the connection between micropolar media

This study proposes two new types of mechanical metamaterials, cosine-function curved column metamaterial (CFCCM) and compression-torsion coupling metamaterial (CTCM), constructed based on cosine-function curved columns (CFCC), which possess broadband tailorable mechanical and vibration suppression performance. The distinction between these two types lies in the integration of a tetra-chiral structure

Bayesian inference has been widely used for updating structural finite element models. However, the high computational cost of evaluating the posterior distribution in conventional Bayesian model updating (BMU) frameworks significantly limits their applicability. In this study, a novel fast variational Bayesian inference method is introduced to reduce the computational cost of BMU. The core of the

The experimental characterization of complex dispersion curves is challenging in phononic crystals, composites, periodic, architected or metamaterials. Recent studies have highlighted the importance of subspace identification methods in determining wave propagation properties through complex wavenumbers and, consequently, in characterizing a complex structure experimentally. Still, such methods have

Nonlinear systems exhibit a plethora of complex dynamic behaviours that are difficult to model and predict accurately. This difficulty often arises from a lack of knowledge of the physics that induces the nonlinear behaviours and the strong sensitivity of the nonlinear dynamics to parameter variation. We introduce in this paper a methodology to carry out nonlinear model updating based on bifurcations

Elastic waves in structural materials typically propagate as a combination of multiple coupled modes, each exhibiting complex frequency-dependent behaviour. This inherent multimodal nature poses a major challenge for precise wave manipulation. While as a subwavelength artificial interface, metasurfaces have shown strong potential in controlling elastic waves, most existing approaches are limited to

A combined experimental-numerical investigation is carried out on the mechanical response of commercial 4 Ah Li-ion pouch cells. Hemispherical indentations were performed across a broad range of conditions, including temperatures from –5 °C to 70 °C, strain rates from 0.001 s⁻¹ to 1000 s⁻¹, and states of charge (SoC) from 0.0 to 0.97. The experiments demonstrate that temperature and strain rate are

This paper systematically investigates the influence of size effect and phase structures on the bending properties of high-entropy alloy (HEA) microcantilevers, providing critical insights for the development and application of micro-/nano-devices. Notably, we report for the first time an unconventional “inverse size effect” (referring to the bending stress behavior rather than a general material strength

High-efficiency noise reduction across an ultra-broadband frequency spectrum remains a significant challenge in acoustic engineering. Conventional passive absorbers are inherently limited in bandwidth, typically requiring complex multilayer or composite configurations to achieve broad spectral coverage. An ultra-broadband acoustic structure that leverages the interference resonance mechanisms in multi-branched

This paper presents a novel hybrid resolved-unresolved and heterogeneous-parallel coupling framework for simulating fluid-particle interactions with non-spherical particles of arbitrary geometries. The framework integrates Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) to dynamically assigns resolved (Immersed Boundary Method) and unresolved (drag force model) coupling schemes

Surface roughness of micro-slow-wave structures in vacuum electron devices significantly affects amplification efficiency owing to skin-depth effects. To investigate the mechanism of milling-induced surface-texture formation, a three-dimensional model was established to predict micro-milling marks. The model incorporated tool-attachment errors into a radial throw amplification framework under high-speed

This study proposes a general and efficient multiple scattering analysis framework based on the thin-layer method to model the dynamic interaction between arbitrarily positioned resonator clusters and elastic half-spaces (including homogeneous and layered media). Using this framework, the Rayleigh wave attenuation performance and mechanisms of four representative resonator configurations are systematically

The fabrication of film cooling holes (FCHs) in turbine blades constitutes a critical technology for resolving the inherent contradiction between thermal resistance requirements of hot-end components and turbine inlet temperature design in advanced aviation engines. The taper angle and wall quality of these holes directly determine the operational service life of blades. In comparison with alternative

Wheel Center Load (WCL) is crucial for assessing the system durability in vehicle health monitoring. Several WCL estimation methods, such as multibody dynamics modeling (MDM), are implemented for the substitution of WCL sensors due to their high cost and complex installation. However, accurate MDM for different types of vehicles is a challenge. This paper aims to design and implement a sparse noncausal

In existing indirect methods for the moving vehicle load (MVL) identification from bridge responses, challenges related to the identification of multiple loads with varying magnitudes remain insufficiently explored. To address these issues, a novel two-stage identification framework is proposed by integrating measurement weights (MWs) and multiple load-specific regularization parameters in this study

Energy harvesting technology enables the conversion of kinetic energy into electrical power, delivering a sustainable energy supply to wireless sensing nodes. Underpinned by high power density, structural adaptability, and operational robustness, electromagnetic energy harvesting has emerged as a highly promising approach for self-powered sensing applications. This paper presents a Dual Magnet Array–Augmented

The reliability of complex electromechanical systems is crucial for industrial and national operations. However, accurately assessing the health state of these systems presents significant challenges due to high-dimensional data, nonstationary conditions, and limited labeled data. To address these challenges, we proposed an unsupervised health state assessment method that integrated an Autoregressive

Artificial intelligence (AI) is reshaping the landscape of modern manufacturing by enabling intelligent, adaptive, and increasingly autonomous production systems. In high-end manufacturing processes, persistent challenges such as complex machine-tool dynamics, variability in equipment performance, and environmental instabilities hinder consistent quality and high efficiency. These challenges are further

To simultaneously address broadband combustion noise and tonal thermoacoustic oscillations, two distinct but sometimes coexisting noise sources in combustors, this study proposes an ultrathin acoustic metaliner capable of attenuating both. The effectiveness of this metaliner’s proof-of-concept design is demonstrated in a prototypical thermoacoustic system: a Rijke tube driven by an electrical heater

High-strength steel (HSS) Q690 offers a superior strength-to-weight ratio for lightweight structural applications but remains susceptible to high-cycle fatigue (HCF) failure. Conventional design codes significantly underestimate its fatigue life, and the scarcity of test data hinders the development of reliable S–N curves. Meanwhile, full-scale fatigue testing is time-consuming and costly, necessitating

Particle-fluid flows are ubiquitous in natural and industrial processes, yet their modeling remains challenging due to the coexistence of flowing and quasi-static granular regions and the computational demands of large-scale systems. The Discrete Particle Method (DPM) offers particle-scale resolution but is computationally prohibitive for large domains. Conversely, the conventional Two-Fluid Model

This study presents enhanced nonlinear features at the sensing terminal for improved fatigue detection through leveraging the wave control capability of metamaterials, utilizing a defect-mode metamaterial (DMM) system. Initially, an analytical investigation based on mass-spring chain models is explored to illustrate the defect cavity concept aiming at improving the current nonlinear ultrasonic sensitivity

Deep learning (DL) has been extensively studied as a powerful data-driven method and shown encouraging results in numerous acoustic imaging-related tasks. Nevertheless, the notable improvements achieved through the advanced DL-based methods often come with increased model parameters and complexity, making the deployment of the models on devices with limited computational capabilities challenging. Knowledge

High-precision three-dimensional (3D) measurement of large complex components (LCCs) such as vehicle bodies provides data benchmark for subsequent robotized manufacturing processes. A huge challenge in LCCs measurement is to register the adjacent point clouds with partial overlap, especially when the point cloud geometric features are weak. Despite the existing sparse iterative closest point (Sparse-ICP)