arXiv subjects

Yitao Wang

Publications and source records attributed to Yitao Wang.

7 recordsLinked to original sources

arXiv2026

STEAM: Self-Supervised Temporal Ensemble Advantage Modeling for Real-World Robot Learning

Real-world robot learning increasingly relies on heterogeneous data, but demonstrations and rollouts often mix useful progress with stalls, corrections, and suboptimal behavior. Effective policy learning therefore requires frame-level advantages that distinguish reliable local progress from failures and regressions. We propose Self-supervised Temporal Ensemble Advantage Modeling (STEAM), a label-free method that learns such advantages from expert demonstrations. STEAM trains an ensemble of temporal-offset predictors on frame pairs within expert trajectories, using the normalized temporal offset between two frames as a self-supervised signal. Each predictor maps a frame pair to a distribution over temporal offsets, which is converted into a scalar advantage. STEAM then takes the minimum advantage across the ensemble to score mixed-quality rollout data conservatively. Across real-world bimanual towel folding, chip checkout, cola restocking, and single-arm pick-and-place tasks, STEAM identifies stalls, failures, and recoveries. When combined with CFGRL, STEAM further improves policy success rate by 59%, 54.3%, 23% and 16.2% over baselines, respectively.

cs.RO↗

arXiv2026

EgoEMG: A Multimodal Egocentric Dataset with Bilateral EMG and Vision for Hand Pose Estimation

Surface electromyography (sEMG) records muscle activity during hand movement and can be decoded to recover detailed hand articulation. EMG and egocentric vision are complementary for hand sensing: EMG captures fine-grained finger articulation even under occlusion and poor lighting, while vision provides global hand configuration. However, no existing dataset synchronizes both modalities. We present EgoEMG, a multimodal egocentric dataset for bimanual hand pose estimation. EgoEMG includes bilateral wristband EMG with 16 total channels (8 per wrist) sampled at 2 kHz, 120 Hz IMU, egocentric wide-angle RGB video, external RGB-D video, and mocap-derived hand motion with wrist articulation angles. The dataset covers 41 participants performing 60 gesture classes, including 30 single-hand gestures and 30 bimanual gestures, totaling more than 10 hours of recording. We also introduce a benchmark with three tasks -- EMG-to-pose, vision-to-pose, and EMG+vision fusion -- under a shared joint-angle prediction target and common generalization split axes (cross-gesture, cross-user, and combined). As baselines, we evaluate EMGFormer for EMG-to-pose and generic ResNet/ViT backbones for vision-to-pose. We further study a residual fusion architecture that improves over matched lightweight vision-only baselines. Together, EgoEMG and its benchmark establish a foundation for future research on multimodal hand pose estimation with EMG and vision.

cs.CV↗

arXiv2026

WristPP: A Wrist-Worn System for Hand Pose And Pressure Estimation

Accurate 3D hand pose and pressure sensing is essential for immersive human-computer interaction, yet simultaneously achieving both in mobile scenarios remains a significant challenge. We present WristPP, a camera-based wrist-worn system that estimates 3D hand pose and per-vertex pressure from a single wide-FOV RGB frame in real time. A Vision Transformer (ViT) backbone with joint-aligned tokens predicts Hand-VQVAE codebook indices for mesh recovery, while an extrinsics-conditioned branch jointly estimates per-vertex pressure. On a self-collected dataset of 133,000 frames (20 subjects; 48 on-plane and 28 mid-air gestures), WristPP attains a Mean Per-Joint Position Error (MPJPE) of 2.9 mm, Contact IoU of 0.712, Volumetric IoU of 0.618, and foreground pressure MAE of 10.4 g. Across three user studies, WristPP delivers touchpad-level efficiency in mid-air pointing and robust multi-finger pressure control on an uninstrumented desktop. In a real-world large-display Whac-A-Mole task, WristPP also enables higher success ratio and lower arm fatigue than head-mounted camera-based baselines. These results position WristPP as an effective, mobile solution for versatile pose- and pressure-based interaction. Website: https://zhenqis123.github.io/WristPP/.

cs.HC↗

arXiv2025

Timing the Escape of a Caged Electron

Charge transfer is fundamentally dependent on the overlap of the orbitals comprising the transport pathway. This has key implications for molecular, nanoscale, and quantum technologies, for which delocalization (and decoherence) rates are essential figures of merit. Here, we apply the core hole clock technique - an energy-domain variant of ultrafast spectroscopy - to probe the delocalization of a photoexcited electron inside a closed molecular cage, namely the Ar 2p54s1 state of Ar@C60. Despite marginal frontier orbital mixing in the ground configuration, almost 80% of the excited state density is found outside the buckyball due to the formation of a markedly diffuse hybrid orbital. Far from isolating the intracage excitation, the surrounding fullerene is instead a remarkably efficient conduit for electron transfer: we measure characteristic delocalization times of 6.6 $\pm$ 0.3 fs and $\lesssim$ 500 attoseconds, respectively, for a 3D Ar@C60 film and a 2D monolayer on Ag(111).

physics.chem-ph↗

arXiv2024

Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models

The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges, particularly due to the domain gap between labeled datasets from well-resourced centers and unlabeled datasets from less-resourced centers. This disparity affects the fairness of artificial intelligence algorithms in healthcare. We introduce Diffuse-UDA, a novel method leveraging diffusion models to tackle Unsupervised Domain Adaptation (UDA) in medical image segmentation. Diffuse-UDA generates high-quality image-mask pairs with target domain characteristics and various structures, thereby enhancing UDA tasks. Initially, pseudo labels for target domain samples are generated. Subsequently, a specially tailored diffusion model, incorporating deformable augmentations, is trained on image-label or image-pseudo-label pairs from both domains. Finally, source domain labels guide the diffusion model to generate image-label pairs for the target domain. Comprehensive evaluations on several benchmarks demonstrate that Diffuse-UDA outperforms leading UDA and semi-supervised strategies, achieving performance close to or even surpassing the theoretical upper bound of models trained directly on target domain data. Diffuse-UDA offers a pathway to advance the development and deployment of AI systems in medical imaging, addressing disparities between healthcare environments. This approach enables the exploration of innovative AI-driven diagnostic tools, improves outcomes, saves time, and reduces human error.

cs.CV↗

arXiv2024

nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

In the field of biomedical image analysis, the quest for architectures capable of effectively capturing long-range dependencies is paramount, especially when dealing with 3D image segmentation, classification, and landmark detection. Traditional Convolutional Neural Networks (CNNs) struggle with locality respective field, and Transformers have a heavy computational load when applied to high-dimensional medical images.In this paper, we introduce nnMamba, a novel architecture that integrates the strengths of CNNs and the advanced long-range modeling capabilities of State Space Sequence Models (SSMs). Specifically, we propose the Mamba-In-Convolution with Channel-Spatial Siamese learning (MICCSS) block to model the long-range relationship of the voxels. For the dense prediction and classification tasks, we also design the channel-scaling and channel-sequential learning methods. Extensive experiments on 6 datasets demonstrate nnMamba's superiority over state-of-the-art methods in a suite of challenging tasks, including 3D image segmentation, classification, and landmark detection. nnMamba emerges as a robust solution, offering both the local representation ability of CNNs and the efficient global context processing of SSMs, setting a new standard for long-range dependency modeling in medical image analysis. Code is available at https://github.com/lhaof/nnMamba

cs.CV↗

arXiv2024

Symmetry breaking and spin-orbit coupling for individual vacancy-induced in-gap states in MoS2 monolayers

Spins confined to point defects in atomically-thin semiconductors constitute well-defined atomic-scale quantum systems that are being explored as single photon emitters and spin qubits. Here, we investigate the in-gap electronic structure of individual sulphur vacancies in molybdenum disulphide (MoS2) monolayers using resonant tunneling scanning probe spectroscopy in the Coulomb blockade regime. Spectroscopic mapping of defect wavefunctions reveals an interplay of local symmetry breaking by a charge-state dependent Jahn-Teller lattice distortion that, when combined with strong (~100 meV) spin-orbit coupling, leads to a locking of an unpaired spin-1/2 magnetic moment to the lattice at low temperature, susceptible to lattice strain. Our results provide new insights into spin and electronic structure of vacancy induced in-gap states towards their application as electrically and optically addressable quantum systems.

cond-mat.mes-hall↗