arXiv subjects

Yufeng Tian

Publications and source records attributed to Yufeng Tian.

6 recordsLinked to original sources

arXiv2026

ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning

Tactile information plays a crucial role in human manipulation tasks and has recently garnered increasing attention in robotic manipulation. However, existing approaches mostly focus on the alignment of visual and tactile features and the integration mechanism tends to be direct concatenation. Consequently, they struggle to effectively cope with occluded scenarios due to neglecting the inherent complementary nature of both modalities and the alignment may not be exploited enough, limiting the potential of their real-world deployment. In this paper, we present ViTaS, a simple yet effective framework that incorporates both visual and tactile information to guide the behavior of an agent. We introduce Soft Fusion Contrastive Learning, an advanced version of conventional contrastive learning method and a CVAE module to utilize the alignment and complementarity within visuo-tactile representations. We demonstrate the effectiveness of our method in 12 simulated and 3 real-world environments, and our experiments show that ViTaS significantly outperforms existing baselines. Project page: https://skyrainwind.github.io/ViTaS/index.html.

cs.RO↗

arXiv2025

A voltage-responsive strongly dipolar-coupled macrospin network with emergent dynamics for computing

Emergent behavior, which arises from local interactions between simple elements, is pervasive in nature. It underlies the energy-efficient computing in our brains. However, realizing such dynamics in artificial materials, particularly under low-energy stimuli, remains a fundamental challenge. While dipole-dipole interactions are typically suppressed in magnetic storage, here we harness and amplify them to construct a strongly dipolar-coupled network of SmCo5 macrospins at wafer scale, which can exhibit intrinsic interaction-driven collective dynamics in response to voltage pulses. The network combines three essential ingredients: strong dipolar coupling by large single-domain macrospin, giant voltage control of coercivity over nearly 1000-fold, and disordered network topology with frustrated Ising-like energy landscape. When stimulated by 1 V pulses, the network enters a regime where interaction-driven magnetic behaviors emerge, including spontaneous demagnetization, greatly enhanced magnetization modulation, reversible freeze and resume evolution and stochastic convergence toward low-energy magnetic configurations. All these behaviors are completely absent at the single-nanomagnet level. Furthermore, by constructing micromagnetic models of the strongly dipolar-coupled macrospin networks, we show that the resulting nonlinear, high-dimensional collective dynamics, intrinsic to strongly-interacting systems, can enable accurate chaotic Mackey-Glass prediction and multiclass drone-signal classification. Our work establishes the voltage-responsive strongly-coupled SmCo5 network as a mesoscopic platform for probing emergent magnetic dynamics previously inaccessible under ambient conditions. It also suggests a fundamental distinct route towards scalable, low-voltage computing, one rooted in native physical interaction-driven collective dynamics at the network level.

cond-mat.mtrl-sci↗

arXiv2025

A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement

Underwater image restoration and enhancement are crucial for correcting color distortion and restoring image details, thereby establishing a fundamental basis for subsequent underwater visual tasks. However, current deep learning methodologies in this area are frequently constrained by the scarcity of high-quality paired datasets. Since it is difficult to obtain pristine reference labels in underwater scenes, existing benchmarks often rely on manually selected results from enhancement algorithms, providing debatable reference images that lack globally consistent color and authentic supervision. This limits the model's capabilities in color restoration, image enhancement, and generalization. To overcome this limitation, we propose using in-air natural images as unambiguous reference targets and translating them into underwater-degraded versions, thereby constructing synthetic datasets that provide authentic supervision signals for model learning. Specifically, we establish a generative data framework based on unpaired image-to-image translation, producing a large-scale dataset that covers 6 representative underwater degradation types. The framework constructs synthetic datasets with precise ground-truth labels, which facilitate the learning of an accurate mapping from degraded underwater images to their pristine scene appearances. Extensive quantitative and qualitative experiments across 6 representative network architectures and 3 independent test sets show that models trained on our synthetic data achieve comparable or superior color restoration and generalization performance to those trained on existing benchmarks. This research provides a reliable and scalable data-driven solution for underwater image restoration and enhancement. The generated dataset is publicly available at: https://github.com/yftian2025/SynUIEDatasets.git.

cs.CV↗

arXiv2025

H$^3$DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning

Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distribution. However, these methods often overlook the critical coupling between visual perception and action prediction. In this work, we introduce $\textbf{Triply-Hierarchical Diffusion Policy}~(\textbf{H$^{\mathbf{3}}$DP})$, a novel visuomotor learning framework that explicitly incorporates hierarchical structures to strengthen the integration between visual features and action generation. H$^{3}$DP contains $\mathbf{3}$ levels of hierarchy: (1) depth-aware input layering that organizes RGB-D observations based on depth information; (2) multi-scale visual representations that encode semantic features at varying levels of granularity; and (3) a hierarchically conditioned diffusion process that aligns the generation of coarse-to-fine actions with corresponding visual features. Extensive experiments demonstrate that H$^{3}$DP yields a $\mathbf{+27.5\%}$ average relative improvement over baselines across $\mathbf{44}$ simulation tasks and achieves superior performance in $\mathbf{4}$ challenging bimanual real-world manipulation tasks. Project Page: https://lyy-iiis.github.io/h3dp/.

cs.RO↗

arXiv2019

Enhanced spin transfer torque in platinum/ferromagnetic-metal structures by optimizing the platinum thickness

Spin transfer torque (STT) driven by a charge current plays a key role in magnetization switching in heavy-metal/ferromagnetic-metal structures. The STT efficiency defined by the ratio between the effective field due to STT and the current density, is required to be improved to reduce energy compulsions in the STT-based spintronic devices. In this work, using the harmonic Hall measurement method, we experimentally studied the STT efficiency in platinum(Pt)/FM structures as a function of the Pt thickness. We found that the STT efficiency strongly depends on the Pt thickness and reaches a maximum value of 4.259 mT/($10^6$A/$cm^{2}$) for the 1.8-nm-thickness Pt sample. This result indicates that competition between spin Hall effect (SHE) and Rashba effect as well as spin diffusion process across the Pt layer determines the Pt thickness for the maximum STT efficiency. We demonstrated the role played by the spin diffusion besides the spin current generation mechanisms in improvement of the STT efficiency, which is helpful in designing STT-based devices.

cond-mat.mtrl-sci↗

arXiv2019

Electrical control of spin mixing conductance in a Y$_3$Fe$_5$O$_{12}$/Platinum bilayer

We report a tunable spin mixing conductance, up to $\pm 22\%$, in a Y${}_{3}$Fe${}_{5}$O${}_{12}$/Platinum (YIG/Pt) bilayer.This control is achieved by applying a gate voltage with an ionic gate technique, which exhibits a gate-dependent ferromagnetic resonance line width. Furthermore, we observed a gate-dependent spin pumping and spin Hall angle in the Pt layer, which is also tunable up to $\pm$ 13.6\%. This work experimentally demonstrates spin current control through spin pumping and a gate voltage in a YIG/Pt bilayer, demonstrating the crucial role of the interfacial charge density for the spin transport properties in magnetic insulator/heavy metal bilayers.

cond-mat.mes-hall↗