Continuum robots possess high flexibility and redundancy, making them well suited for safe interaction in complex environments, yet their continuous deformation and nonlinear dynamics pose fundamental challenges to perception, modeling, and control. Existing vision-based control approaches often rely on end-to-end learning, achieving shape regulation without explicit awareness of robot geometry or its interaction with the environment. Here, we introduce a shape-interpretable visual self-modeling framework for continuum robots that enables geometry-aware control. Robot shapes are encoded from multi-view planar images using a Bezier-curve representation, transforming visual observations into a compact and physically meaningful shape space that uniquely characterizes the robot's three-dimensional configuration. Based on this representation, neural ordinary differential equations are employed to self-model both shape and end-effector dynamics directly from data, enabling hybrid shape-position control without analytical models or dense body markers. The explicit geometric structure of the learned shape space allows the robot to reason about its body and surroundings, supporting environment-aware behaviors such as obstacle avoidance and self-motion while maintaining end-effector objectives. Experiments on a cable-driven continuum robot demonstrate accurate shape-position regulation and tracking, with shape errors within 1.56% of image resolution and end-effector errors within 2% of robot length, as well as robust performance in constrained environments. By elevating visual shape representations from two-dimensional observations to an interpretable three-dimensional self-model, this work establishes a principled alternative to vision-based end-to-end control and advances autonomous, geometry-aware manipulation for continuum robots.
Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots
Department of Cognitive Robotics, Delft University of Technology（认知机器人学系，代尔夫特理工大学）；Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology（计算机科学与人工智能实验室（CSAIL），麻省理工学院）；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（机器人与机电研究所，德国航空航天中心（DLR））；Department of Industrial Engineering, University of Trento（工业工程系，特伦托大学）
CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making
Eigenlogic: Interpretable Quantum Observables with applications to Fuzzy Behavior of Vehicular Robots
CentraleSupélec（中央理工-高等电力学院）；Laboratoire des Signaux et Systèmes（信号与系统实验室）；Université Paris-Saclay（巴黎-萨克雷大学）；AFSCET（法国系统科学协会）；Conservatoire National des Arts et Métiers（法国国立工艺学院）
Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots
The Robotics Center and the Kahlert School of Computing at the University of Utah（犹他大学机器人中心和Kahlert计算学院）；The Departments of Computer Science and Electrical and Computer Engineering at Vanderbilt University（范德比大学计算机科学与电气与计算机工程系）
Towards Interpretable Visuo-Tactile Predictive Models for Soft Robot Interactions
The BioRobotics Institute, Sant’Anna School of Advanced Studies（圣安娜高级研究院生物机器人研究所）；Departement of Excellence in Robotics & AI, Sant’Anna School of Advanced Studies（圣安娜高级研究院机器人与人工智能卓越系）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Neural Configuration Distance Function for Continuum Robot Control
Contextual Robotics Institute, University of California San Diego（加州大学圣地亚哥分校情境机器人学研究所）；Robotics Department, University of Michigan（密歇根大学机器人学系）；Toyota Motor North America, Research & Development（丰田北美研发中心）
Modeling, control, and stiffness regulation of layer jamming-based continuum robots
Department of Electrical Engineering, Polytechnique Montréal and GERAD（电气工程系，蒙特利尔理工学院及GERAD）；Robotics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（机器人研究所，工程与信息技术学院，技术学院悉尼大学）
Interpretable Robot Control via Structured Behavior Trees and Large Language Models
Automation and Robotics Research Group (ARG), Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg（自动化与机器人研究组（ARG）、安全、可靠性与信任跨学科中心（SnT）、卢森堡大学）
Learning-based Control for Tendon-Driven Continuum Robotic Arms
K. N. Toosi University of Technology（柯尼托西理工大学）；Center of Excellence in Robotics and Control（机器人与控制卓越中心）；Advanced Robotics and Automated Systems (ARAS)（高级机器人与自动化系统（ARAS））
College of Engineering, Peking University（北京大学工学院）；School of Mechanical Engineering, Northwestern Polytechnical University（西北工业大学机械工程学院）；School of Astronautics, Northwestern Polytechnical University（西北工业大学航空学院）
Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Shanghai Ruijin Rehabilitation Hospital（上海瑞金康复医院）
An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion Learning
Vidyasirimedhi Institute of Science and Technology（维达亚希米德科学与技术学院）；The Mærsk Mc-Kinney Møller Institute（马士基麦金尼·莫勒研究所）；University of Southern Denmark（南方丹麦大学）
Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments
Southern University of Science and Technology（南方科技大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）；City University of Hong Kong（香港城市大学）
Discrete Geometric Modeling and Extended State Estimation of Continuum Robots
Technical University of Munich, TUM School of Engineering and Design（慕尼黑技术大学，工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Chair of Automatic Control（自动控制教授席）
