Robot-assisted endovascular navigation provides significant advantages, including reduced radiation exposure for surgeons and improved patient safety. However, a major challenge is to control curvilinear instruments like guidewires precisely for smooth and accurate navigation while adapting to anatomical variations and external forces. Traditional segmentation-based approaches struggle with real-time prediction of the guidewire’s evolving shape, limiting their effectiveness in navigation tasks. In this paper, we propose SplineFormer, an explainable transformer network that predicts the continuous, structured representation of the guidewire as a B-spline. This formulation enables a compact, smooth, and explainable state representation that facilitates downstream navigation. By leveraging SplineFormer’s predictions within an imitation learning framework, our system successfully performs autonomous endovascular navigation. Experimental results show that SplineFormer achieves a 50% success rate when fully autonomously cannulating the Brachiocephalic Artery in a real robotic setup, demonstrating its potential for improved autonomous navigation in endovascular interventions.

We demonstrate Rock, a system for cleaning relational data. Rock highlights the following unique features: (1) it extends logic rules by embedding machine learning models as predicates, to benefit from both ML and logic deduction; (2) it supports entity resolution, conflict resolution, timeliness deduction and missing data imputation in a unified process; and (3) it provides parallelly scalable algorithms for rule discovery, error detection and error correction, in batch and incremental modes. We will demonstrate Rock for its (a) easy-to-use interface, (b) scalability when cleaning large datasets, (c) accuracy for detecting and correcting errors across multiple tables, and (d) applications at banks and HR departments.