Continuum Robot Control

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Continuum robot control aims to move a flexible robot’s tip or regulate its whole-body shape despite underactuation, deformation, and contact with the environment. Approaches range from Cosserat-rod and piecewise-constant-strain models with model-predictive or visual-servo control to learned dynamics, Koopman models, and reinforcement-learning policies. Camera-based and embedded sensing support feedback in settings such as surgical navigation, cluttered manipulation, locomotion, and aerial interaction, while safety controllers help manage collisions. A central challenge is balancing physically accurate models and reliable state estimation with the speed needed for real-time control, especially under contact, changing payloads, and sim-to-hardware transfer.

Latest papers

71 papers4 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Modeling and Control of a Pneumatic Soft Robotic Catheter Using Neural Koopman Operators
Mar 4, 2026Yiyao Yue, Noah Barnes, Lingyun Di +4Robotic Endovascular Intervention Continuum Robot Control
Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA. · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA. · Department of Mechanical Engineering, University of Maryland, College Park, MD, USA.
Shape-Interpretable Visual Self-Modeling Enables Geometry-Aware Continuum Robot Control
Mar 2, 2026Peng Yu, Xin Wang, Ning TanContinuum Robot Control Vision-Based Robot Control
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.
3D Shape Control of Extensible Multi-Section Soft Continuum Robots via Visual Servoing
Feb 22, 2026Abhinav Gandhi, Shou-Shan Chiang, Cagdas D. Onal +1Continuum Robot Control Vision-Based Robot Control
Robotics Engineering Department, Worcester Polytechnic Institute 100 Institute Rd, Worcester, MA 01609, USA
Systematic Analysis of Coupling Effects on Closed-Loop and Open-Loop Performance in Aerial Continuum Manipulators
Feb 21, 2026Niloufar Amiri, Shayan Sepahvand, Iraj Mantegh +1Continuum Robot Control Aerial Manipulation
Department of Mechanical, Industrial, and Mechatronics Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada · Aerospace Research Center, National Research Council of Canada (NRC), Montreal, QC H3T 2B2, Canada
Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators
Feb 20, 2026Carina Veil, Moritz Flaschel, Ellen Kuhl +1Inverse Kinematics Continuum Robot Control
Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA · Institute of Applied Mechanics, Friedrich-Alexander-Universität Erlangen–Nürnberg, 91058 Erlangen, Germany · Cognitive Robotics Department, Delft University of Technology, 2628 Delft, The Netherlands
Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped
Feb 18, 2026Saumya Karan, Neerav Maram, Suraj Borate +1Quadruped Locomotion Soft Robotics
IIT Gandhinagar, Department of Electrical Engineering, Gujarat, India · Sreenidhi Institute of Science and Technology, Department of Electronics and Computer Engineering, Telangana, India
MarUco: A Markerless 6D Pose Estimation Framework for Closed-Loop Control of Surgical Continuum Manipulators
Feb 18, 2026Junhyun Park, Chunggil An, Myeongbo Park +3Object Pose Estimation Continuum Robot Control
Department of Robotics and Mechatronics Engineering, DGIST, Daegu 42988, Republic of Korea · AI Research Lab, DEEPNOID, Seoul, Republic of Korea
Real-time Monocular 2D and 3D Perception of Endoluminal Scenes for Controlling Flexible Robotic Endoscopic Instruments
Feb 16, 2026Ruofeng Wei, Kai Chen, Yui Lun Ng +7Robotic Endoscopy Robotic Perception
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China. · Department of Mechanical Engineering, The University of Hong Kong, China. · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, and also with the Department of Mechanical Engineering, The University of Hong Kong.
Soft Swing-up: Benchmarking Model-Based Optimal Control for Rigid-Soft Underactuated Systems
Feb 3, 2026Daniele Caradonna, Nikhil Nair, Anup Teejo Mathew +5Robotic Control Model Predictive Control
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy · Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy · Department of Cognitive Robotics, Delft University of Technology, 2628 CN Delft, The Netherlands +3
Collaborative Continuum Robots: A Survey from an Automation-System Perspective
Dec 22, 2025Xinyu Li, Qian Tang, Guoxin Yin +4Multi-Robot Coordination Continuum Robot Control
Rapidly Learning Soft Robot Control via Implicit Time-Stepping
Nov 10, 2025Andrew Choi, Dezhong Tong, Xiaonan HuangRobot Policy Learning Robotics Simulation
Horizon Robotics, Cupertino, CA, USA · Robotics Department at the University of Michigan, Ann Arbor, MI, USA

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Modeling and Control of a Pneumatic Soft Robotic Catheter Using Neural Koopman Operators

Yiyao Yue, Noah Barnes, Lingyun Di, Olivia Young+3

Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA. · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA. · Department of Mechanical Engineering, University of Maryland, College Park, MD, USA.

Robotic Endovascular Intervention Continuum Robot Control Soft Robotics Koopman Operator Learning

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Catheter-based interventions are widely used for the diagnosis and treatment of cardiac diseases. Recently, robotic catheters have attracted attention for their ability to improve precision and stability over conventional manual approaches. However, accurate modeling and control of soft robotic catheters remain challenging due to their complex, nonlinear behavior. The Koopman operator enables lifting the original system data into a linear "lifted space", offering a data-driven framework for predictive control; however, manually chosen basis functions in the lifted space often oversimplify system behaviors and degrade control performance. To address this, we propose a neural network-enhanced Koopman operator framework that jointly learns the lifted space representation and Koopman operator in an end-to-end manner. Moreover, motivated by the need to minimize radiation exposure during X-ray fluoroscopy in cardiac ablation, we investigate open-loop control strategies using neural Koopman operators to reliably reach target poses without continuous imaging feedback. The proposed method is validated in two experimental scenarios: interactive position control and a simulated cardiac ablation task using an atrium-like cavity. Our approach achieves average errors of 2.1 +- 0.4 mm in position and 4.9 +- 0.6 degrees in orientation, outperforming not only model-based baselines but also other Koopman variants in targeting accuracy and efficiency. These results highlight the potential of the proposed framework for advancing soft robotic catheter systems and improving catheter-based interventions.