CORTEXA
← Browse
arxiveess.SYcs.AIcs.RO2026-07-23

Interaction Dynamics Modeling and Predictive Control for Safe Steerable Catheter--Tissue Interaction

Yongyan Cao

Safe steerable catheter control is fundamentally a problem of interaction dynamics: the tip must follow a planned motion, remain compliant against moving tissue, reject friction and hysteresis, and respect a clinically meaningful never-exceed contact-force bound. We formulate catheter--tissue interaction dynamics in the scalar tip-normal coordinate of a single-segment single-tendon catheter. A partial-physics feedforward cancels only the reliable nominal bending dynamics, exposing a configuration-invariant linear interaction-dynamics model whose input gain varies through the scalar catheter inertia. A predictive optimizer then regulates this interaction state subject to hard contact-force, tendon-force, and curvature constraints. An augmented Kalman filter compresses contact, friction, and modeling error into one sensor-free disturbance state, giving nominal offset-free regulation in free space while leaving force safety to the explicit constraint. The unconstrained and disturbance-free limit recovers classical catheter impedance as a special realization of the same interaction dynamics, rather than as the main design object. In a MuJoCo distributed-compliance simulation of an eight-link tendon-driven catheter, disturbance augmentation cuts free-space approach error by 90\%, and only the force-constrained predictive interaction-dynamics controller reconciles tracking with the 0.5\,N bound: the unconstrained controller drives contact force to 0.60\,N against a penetrating target, while the constrained one holds 0.47\,N at identical tracking. These results show that offset-free motion regulation and contact-force safety are coupled interaction-dynamics objectives, and that the explicit predictive constraint resolves their tension under stiff tissue contact. The bound also holds under $0.5$\,mm, $1.2$\,Hz cardiac motion. Hardware validation is future work.

View free PDFSource page

Related papers

arxivcs.ROcs.AIcs.LGeess.SYmath.OC2026-07-16

Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control

Jihoon Hong, Julian Skifstad, Qiyue Dai, Alice Chan, Glen Chou

World Action Models (WAMs) enable semantically- and physically-informed control but are brittle under distribution shift. In this work, we use mechanistic interpretability to study how robustness-relevant perturbations are represented in WAM activation space. Comparing activation…

View free PDFSource page
arxivcs.ROcs.AIeess.SY2026-07-01

From World Models to World Action Models: A Concise Tutorial for Robotics

Xiaoxiong Zhang, Xiong Zeng, Wei Zhang

World models are increasingly used in embodied intelligence and generative simulation, yet their scope remains ambiguous across communities. This tutorial presents a design-space view of world models as action-conditioned predictive models that estimate the future evolution of ta…

View free PDFSource page
arxiveess.SYcs.AIcs.LGcs.ROmath.OC2026-07-01

GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics

Jeffrey Fang, Keyi Shen, Anutam Srinivasan, Glen Chou

This paper studies real-time robust optimal control for uncertain nonlinear systems, where linear time-varying (LTV) approximations make planning tractable but require sound linearization error bounds (LEBs) to guarantee robust constraint satisfaction. We develop tight, different…

View free PDFSource page
arxivcs.ROcs.AIeess.SY2026-07-01

Adaptive Companionship for Group-Following Robots: Handling Dynamically Changing Group Formations

Cong-Thanh Vu, Yen-Chen Liu

Accompanying a group of humans is an essential aspect of developing human-like social cognition in robots. However, human groups typically do not follow fixed formations, which poses significant challenges for robots in maintaining natural companionship behaviors. In this paper,…

View free PDFSource page