Yang Li

Benjamin Jackson, Nikola Fischer, Harry Robershaw et al.

Objective: To evaluate robotic controller interfaces for interventional neuroradiology procedures in-vitro incorporating a force-sensing platform to assess safety. Methods: A custom endovascular robot, device-mimicking controller, and sensorized neurovascular phantom were developed. Ten interventional neuroradiologists (4 novices, 6 experts) performed simulated navigations using four control modalities: device-mimicking controllers with and without haptic feedback, joystick-based input, and manual navigation. Navigation time, peak vessel-wall forces, incorrect catheterisations, and prolapse events were assessed, alongside user analyses. Results: Manual navigation was fastest (mean 47.7 s) compared to haptic-on (248.7 s), haptic-off (314.7 s), and joystick (392.6 s) modalities (p<0.001). Regardless of controller type, vessel-wall forces were below the 0.70 N puncture threshold; therefore all modalities were considered safe. Joystick produced significantly more prolapse events than manual control (1.56 vs 0.13; p=0.018). Operator experience was relevant to performance: experts made fewer incorrect catheterisations than novices (0.25 vs 0.62; p=0.035) and applied less vessel-wall force (p<0.0005); these effects were sustained across controllers but accentuated when haptics were on. Users perceived haptic on and haptic off as similarly intuitive, and more intuitive than joystick (p=0.033). Conclusion: Device-mimicking robotic controllers outperform joystick interfaces on most metrics; haptic feedback shows promising but non-significant performance benefits.

Zihao Zhang, Aming Wu, Yang Li et al.

In this paper, we study Single-Domain Generalized Object Detection (Single-DGOD), which aims to transfer a detector trained on a single source domain to multiple unseen domains. Existing methods mainly rely on simulation-driven strategies, such as data augmentation or textual prompts, to enlarge the training distribution. However, finite simulations can hardly cover the dynamic variations of real-world scenarios, often causing overfitting to synthetic styles and limited robustness to complex structural degradations. Inspired by the manifold hypothesis, we argue that semantic features, despite diverse visual changes, should lie on a compact and stable low-dimensional manifold. Therefore, robust generalization requires rectifying deviant samples back to this semantic manifold, rather than exhaustively simulating external perturbations. To this end, we propose Manifold Regression with Visual-Text Dual Chain-of-Thought (MR-DCoT), which formulates unknown-domain generalization as a manifold regression problem. MR-DCoT first uses a Visual-Text Dual Chain-of-Thought module to combine VLM-guided semantic evolution with diffusion-based structural perturbation, generating structured off-manifold hard examples. It then introduces Class-Specific Prototype Anchoring to learn a rectification operator that projects deviant features toward the source semantic manifold. By integrating outlier generation and semantic correction into a closed loop, MR-DCoT effectively narrows the distribution gap and improves robustness under unseen shifts. Extensive experiments on three complementary benchmarks, including adverse-weather detection, real-to-art generalization, and zero-shot semantic segmentation, demonstrate the effectiveness and versatility of our method.