A Unified Multi-Dynamics Framework for Perception-Oriented Modeling in Tendon-Driven Continuum Robots

TL;DR

The paper tackles the challenge of perception in tendon-driven continuum robots by proposing a unified multi-dynamics framework that tightly couples motor electrical dynamics, motor–winch transmission, and continuum robot dynamics. By deriving end-to-end models and providing a practical implementation, the approach reveals electromechanical signatures encoded in intrinsic motor signals, enabling perception without extensive external sensing. The method is validated through sim-to-real alignment on SpiRob, capturing hysteresis, delay, and contact-induced transients, and is demonstrated across passive and active perception tasks as well as object-size estimation using a physics-informed ensemble. The work advances perception in soft robotics by reducing hardware complexity while maintaining reliable interaction understanding, with practical impact in safe, compliant manipulation in unstructured environments. Future work is directed at full state estimation and more complex interaction scenarios.

Abstract

Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance. However, their perception often depends on external sensors, which increase hardware complexity and limit scalability. This work introduces a unified multi-dynamics modeling framework for tendon-driven continuum robotic systems, exemplified by a spiral-inspired robot named Spirob. The framework integrates motor electrical dynamics, motor-winch dynamics, and continuum robot dynamics into a coherent system model. Within this framework, motor signals such as current and angular displacement are modeled to expose the electromechanical signatures of external interactions, enabling perception grounded in intrinsic dynamics. The model captures and validates key physical behaviors of the real system, including actuation hysteresis and self-contact at motion limits. Building on this foundation, the framework is applied to environmental interaction: first for passive contact detection, verified experimentally against simulation data; then for active contact sensing, where control and perception strategies from simulation are successfully applied to the real robot; and finally for object size estimation, where a policy learned in simulation is directly deployed on hardware. The results demonstrate that the proposed framework provides a physically grounded way to interpret interaction signatures from intrinsic motor signals in tendon-driven continuum robots.

Figures (11)

• Figure 1: (A) Commanded motor current producing motor torque and tendon tension that drives the robot to deform. (B) Investigated Soft Robot - SpiRob.
• Figure 2: Schematic of the multi-dynamics modeling framework.
• Figure 3: Proposed numerical implementation of the multi-dynamics framework.
• Figure 4: Platform setup: (A) Experimental continuum robotic system; (B) Simulated counterpart via multi-dynamics modeling framework.
• Figure 5: Hysteresis and delay validation.