Tendon-Driven Actuation

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7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 28

Oct 7, 2026cs.RO

Design of a Fully Actuated 4-DOF Robotic Finger With Joint-Specific Hybrid Remote Actuation

This paper presents a fully actuated 4-DOF robotic finger using a joint-specific hybrid remote-actuation architecture. The metacarpophalangeal (MCP) joint is driven by two coordinated rigid-link transmission sets, whereas the proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints are independently actuated by closed-loop wire transmissions incorporating circular rolling-contact joints (RCJs). A larger transmission radius is used at the PIP joint than at the DIP joint. The RCJ wire geometry maintains the total wire-loop length during joint rotation, and the DIP wire routing is designed so that PIP motion does not affect its differential actuation for a fixed MCP configuration. The distal wire transmission, MCP linkage, and fingertip kinematics are analytically modeled. In experiments, the transmission behavior was quantitatively evaluated from the ball-screw displacements measured using ArUco-marker tracking. MCP actuation produced measurable displacements of the PIP and DIP transmission units, whereas isolated PIP and DIP actuation with the MCP fixed supported the intended mechanical decoupling between the distal transmissions. The mean peak fingertip forces under isolated MCP, PIP, and DIP actuation were 21.28 N, 9.22 N, and 5.75 N, respectively. The resulting finger postures were also examined using objects of different geometries and sizes.

Hyojae Kang, Hyun-mok Jung, Joonho Lee +4

Advanced Robotics Research Center, Korea Institute of Machinery & Materials (KIMM) · Mechanical Engineering, University of Science & Technology (UST)

Robotic Hand Design Tendon-Driven Actuation

Oct 7, 2026cs.RO

Design optimization of tendon-driven robots considering tendon wrapping and shortcut

This study proposes a multi-objective optimization method for tendon-driven arm design, addressing the inherent trade-off between joint torque and arm thickness through tendon wrapping and shortcut effects. We formulate the problem to simultaneously maximize torque and minimize thickness, solved using NSGA-II. The algorithm optimizes tendon routing, pulley configurations, and attachment points. Results reveal Pareto-optimal designs with effective moment arm expansion via strategic shortcuts, providing practical guidelines for compact high-torque arms. The optimized configurations demonstrate non-trivial patterns beyond conventional design intuition, offering new insights for engineering efficient robotic systems.

Yuta Sahara, Kento Kawaharazuka, Kei Okada

Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. · AI Center, Graduate School of Information Science and Technology, The University of Tokyo, Japan.

Tendon-Driven Actuation

Sep 29, 2026cs.RO

Closed-Form Cartesian Forward Kinetostatics for Spatial Multi-Segment Tendon-Driven Continuum Robots

Forward kinetostatics of spatial tendon-driven continuum robots typically requires a nonlinear equilibrium solve for each actuation input. This paper develops a force-to-Cartesian-configuration model with a closed-form solution in quadratures for spatial multi-segment robots under tendon actuation. The Cartesian backbone centerline and accumulated material twist serve as generalized coordinates, from which the strain measures and tendon geometry are derived. Variational equilibrium yields explicit axial and bending relations and establishes zero equilibrium material twist within the proposed model for admissible longitudinal non-helical routing. The solution is propagated segment by segment without an iterative equilibrium solve, while retaining axial deformation, spatially varying axial and bending stiffnesses and tendon-routing diameter, and segment-dependent tendon participation. Numerical comparisons with a full-strain geometric variable-strain model (GVS) yield maximum length-normalized tip-position discrepancies of 8.91 x 10^-6 and 1.01 x 10^-5 for the single- and three-segment robots, respectively. Mean evaluation times of 1.52 μs and 2.94 μs, with corresponding speedups of approximately 1864x and 3348x over the baseline, demonstrate the computational advantage of the explicit force-to-configuration mapping in the reported benchmark.

Ke Wu, Fangju Yang, Xiaohui Zhang +4

Robotics Continuum Robot Control Tendon-Driven Actuation

Sep 28, 2026cs.RO

Design and Validation of an Antagonistic Tendon-Driven Dexterous Robotic Hand with Bidirectional Operation

Dexterous robotic hands typically reproduce human hand morphology but inherit its one-sided grasping workspace, requiring wrist or arm reorientation to grasp from the opposite side. Existing reversible hands generally rely on non-anthropomorphic, soft, or task-specific finger arrangements, whereas conventional five-digit anthropomorphic hands remain designed primarily for palmar-side grasping. This paper presents an anthropomorphic, human-scale (200 mm length), lightweight (220 g), 3D-printed, 17-DoF robotic hand built on a bidirectional antagonistic tendon-routing mechanism, in which flexion/extension (except the coupled joint) and abduction/adduction at joints are actively driven without passive return springs. The proposed routing mechanism allows all degrees of freedom to cross their neutral configuration and form grasp closures on either the palmar or dorsal side. Experimental evaluation demonstrates an average motor-to-joint transmission error of 3.0%, an average joint transmission bandwidth of 13.2 Hz, a maximum fingertip force of 29 N, and a positioning repeatability up to 0.15 mm. The hand further achieves a Kapandji score of 8, successfully performs all 33 GRASP Taxonomy grasp types, and performs palmar- and dorsal-side grasping tasks, validating bidirectional operation in a compact, human-scale platform.

Chunghyeon Lee, Hyukjun Kwon, Sungeon Kim +2

Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA · Department of Mechanical Engineering, Sogang University, Seoul 04107, South Korea

Robotic Hand Design Robotic Grasping Dexterous Manipulation Tendon-Driven Actuation

Sep 24, 2026cs.RO

A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning

Jellyfish-inspired robots offer a compliant and efficient approach to underwater locomotion, but achieving large deformation together with repeatable actuation and closed-loop control remains challenging. In this work, we present a tendon-driven robotic jellyfish with constrained soft actuation. Each actuator combines a flexible substrate with discrete constraints, enabling bending up to

150^\circ

with an approximately linear tendon displacement-bending relationship. Eight actuators driven by four servos allow the robot to perform stable swimming, attitude adjustment, and self-righting. Based on the linear actuation, a reinforcement-learning controller is further developed, enabling closed-loop depth regulation in both simulation and physical experiments. These results show that mechanical constraints can improve the controllability of soft actuation while preserving compliant jellyfish-like motion, providing a route toward manoeuvrable and autonomous jellyfish robots.

Jiarui Peng, Yutong Wu, Zelong Wang +5

Hong Kong Embodied AI Lab, Hong Kong SAR, China · The Chinese University of Hong Kong, Hong Kong SAR, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China

Underwater Robotics Tendon-Driven Actuation Soft Robotics

Sep 22, 2026cs.RO

Shaft-Configuration-Adaptive Catheter Tip Position Estimation via Motor-History Conditioned Residual Learning

Tendon-driven continuum manipulators are widely used in medical applications, where accurate tip-position estimation is essential for precise navigation and instrument positioning. However, patient anatomy and procedural setup impose task-dependent unknown shaft configurations, while friction, slack, and compliance introduce hysteresis, making tip estimation challenging. This paper presents a motor-history-conditioned gated recurrent unit (GRU) residual estimator for three-dimensional catheter tip estimation without direct shaft-configuration sensing. First, an initial multidirectional sweep strategy is applied to calibrate a geometric catheter model backbone, and encode the motor-angle and drive-torque response into a shaft-configuration context vector. During subsequent motion, the context conditions a GRU that predicts a task-space residual correcting this backbone, relying on motor measurements alone. The context remains fixed for the current shaft configuration, while the recurrent state captures the evolving actuation history. Across four disposable intra-cardiac echocardiography catheters and 16 bent shaft configurations, the method achieves 3.3mm open-loop tip RMSE, a 59% reduction relative to the constant-curvature baseline.

Peihan Zhang, Michael C. Yip, Ankur Kapoor +1

Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA. · Department of Electrical and Computer Engineering, University of California San Diego, United States.

Residual Learning Robotics Tendon-Driven Actuation Robot State Estimation

Sep 22, 2026cs.RO

History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots

Deterministic dynamics modeling of tendon-driven continuum robots remains challenging owing to uncertainties in material behavior, tendon transmission, friction, and contact. Measured joint configurations and nominal tendon commands do not fully characterize these internal mechanical factors, leaving uncertainty in the subsequent motion. We therefore develop a history-conditioned, physics-informed flow-matching framework for probabilistic dynamics prediction, using motion and actuation histories to predict the distribution of the next complete joint configuration. By recursively sampling next-step configurations under prescribed commands, the model predicts distributions of future whole-body motions. In simulation, scenario-specific models achieve five-second trajectory Energy Scores (lower is better) of 12.05 mm under internal friction variation and 9.29 mm under unobserved actuation disturbances. Relative to the conditional variational autoencoder and diffusion baselines, Flow attains lower Energy Scores and coverage closer to the nominal level in both scenarios. Ablations support history and structural conditioning in both scenarios. On the physical robot, predictions under two tendon-command profiles excluded from training capture the principal motion sequences, with five-second Energy Scores of 11.91 and 11.42 mm, lower than the compared baselines. The predicted-to-measured spread ratios are 1.65 and 1.22 (closer to 1 is better). These results support history-conditioned probabilistic dynamics prediction under incomplete mechanical observations.

Hang Yang, Tingcong Liu, Junjie Xiong +2

Flow Matching Robot Dynamics Learning Tendon-Driven Actuation Soft Robotics

Sep 22, 2026cs.RO

A Reconfigurable Bidirectional Cable-Driven Hip Exoskeleton with Swappable Bench/Backpack Dual-configuration Actuation

Hip exoskeletons provide an important hardware basis for lower-limb rehabilitation and locomotor assistance. Laboratory rehabilitation assessment and system development require substantial actuation and computing resources, whereas mobile assistance requires untethered portability. Integrating both capabilities within one reusable platform remains a central design challenge. This paper presents a reconfigurable bidirectional cable-driven hip exoskeleton platform that rapidly switches between bench-mounted and backpack-mounted actuation while sharing one cable-free wearable hip interface. The platform modularly adapts the actuation configuration, end-effector sensing path, and low-level control interface. Each cable-driven end-effector weighs 0.405 kg, excluding the cable and actuation unit, and integrates an encoder and a torque sensor; experiments validated bench-mounted admittance-based motion tracking capability and backpack-mounted open-loop torque tracking. Human-worn experiments with three healthy participants used myoMOTION to evaluate the platform's wearable-side hip-motion sensing capability, verified bench-to-backpack and backpack-to-bench motion-ready switching across 30 trials in

30.1\pm16.3

s, and formed a small-scale multimodal wearable-exoskeleton gait dataset for sensing validation and data-driven algorithm development, comprising 8 min bench-mounted treadmill records and 11 min backpack-mounted outdoor walking records. These results show that, by unifying the wearable structure, actuation interface, and sensing path, the proposed platform enables validation of the same hip exoskeleton in both bench-mounted and backpack-mounted configurations, providing reusable hardware for iterative development and applications across scenarios.

YuanLong Ji, Shuhan Xiang, Qihan Ye +1

School of Biological Science and Medical Engineering, Beihang University; the Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education; Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology; National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), Beihang University, Beijing, 100191, China

Robotics Assistive Robotics Tendon-Driven Actuation

Sep 20, 2026cs.RO

Design and Control of a Cable-Driven Switchable Actuator with Torque/Tension Dual Modes for Exoskeletons

Existing wearable exoskeleton architectures are typically constrained by a single mechanical output modality, providing either joint torque around an anatomical joint or linear traction along a limb-training-oriented direction, which limits adaptability to diverse training scenarios. This letter presents a cable-driven switchable actuator (CDSA) that can rapidly switch between torque and tension modes while centralizing all sensing and actuation components at the proximal drive unit. A Coupled Movable Pulley Mechanism (CMPM) provides tension amplification at the distal end-effector, while a bidirectional Cable-Driven Ratchet Mechanism (CDRM) enables mode switching and preload regulation. To eliminate the need for distal instrumentation, multi-source proximal sensors are integrated with a data-driven fusion model to estimate distal output forces. An adaptive dual-mode force control strategy based on iterative learning control (ILC) is further developed. Platform experiments demonstrate transmission efficiencies of

(92.4 \pm 2.0)\%

and

(96.5 \pm 3.3)\%

in the torque and tension modes, respectively, along with a tension amplification ratio of

2.77 \pm 0.10

under tension mode. Tracking tests on simulated knee-joint gait trajectories and short-stroke tension profiles yield stable control, with RMSEs of

(4.52 \pm 0.51)\%

and

(3.15 \pm 0.19)\%

of the uncontrolled peak value, respectively. Finally, seated human-coupled experiments validate the system's controllable force generation in both joint-torque and linear-traction application modes.

YuanLong Ji, Xu Liu, Xinyuan Cai +8

School of Biological Science and Medical Engineering, Beihang University; the Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education; Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology; National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), Beihang University, Beijing, 100191, China · School of Engineering Medicine, Beihang University, Beijing, 100191, China · School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, China

Robotic Control Robotics Assistive Robotics Tendon-Driven Actuation Adaptive Control

Aug 28, 2026cs.RO

Aero Hand Open: A Simulation-Ready Tendon-Driven Hand for Dexterous Manipulation Learning

Tendon-driven hands are anthropomorphic, and moving the actuators off the joints is what makes a hand of this capability affordable to build. Two effects produce that saving. Routing force through a cable removes the requirement that a motor fit inside the joint it drives, so smaller and cheaper motors suffice, and one motor can drive several joints through a single cable, so fewer motors are needed. They are also harder to learn on than a direct-drive hand. The underactuated transmission that produces the saving is itself difficult to represent in a simulator, and the joints one cable drives are not independently commandable. We present Aero Hand Open, a tendon-driven anthropomorphic hand that is released simulation-ready. Three things ship with it. A simulation model reproduces the cable transmission itself. An identified actuation map connects that model to the motor commands in both directions, including the three-way coupling of the thumb. A reinforcement learning package trains policies for the hand. Together they let a policy be trained entirely in simulation and run on the hand with no fine-tuning and no state estimation. We release the mechanical design, the simulation model, the identified mapping, the training environment and the deployment stack.

Nan Wang, Mohit Yadav, Jonathan Wulff +5

Chestnut Robotics

Robotic Hand Design Robotics Simulation Sim-to-Real Transfer Robotic Manipulation Tendon-Driven Actuation

Aug 6, 2026cs.RO

A Disturbance in the Force: Force Actuation on the RAVEN II Surgical Robot with Parallel Motor-Cable Units

Difficulty in haptic feedback for surgical robots has been a long-term problem for decades. In recent years, learning-based force estimation from robot states suggests desirable accuracy without the necessity of extra sensors. However, challenges remain in obtaining representative training data in which the robot moves in the workspace under various external forces. In this work, a parallel motor-cable system is developed. With six motor-cable units installed around the robot workspace, cables with controllable tension connected to the robot end-effector can provide the desired external force without interfering with the movement of the surgical robot. The development of the system includes motor-unit hardware, control software, sensor drivers, simulations, and more. Preliminary experiments suggest an accuracy of force actuation with errors less than 1 N.

Haonan Peng, Dun-Tin Chiang, Jordan Hendricks +5

University of Washington, Seattle, WA 98195, USA · Mount Holyoke College, South Hadley, MA 01075, USA

Robotics Tendon-Driven Actuation Surgical Robotics

Jul 28, 2026cs.RO

Modular Robotic Catheters for Endovascular Aneurysm Repair

Fenestrated/Branched endovascular aneurysm repair (FEVAR/BEVAR) require surgeons to navigate catheters and guidewires into various branches of the abdominal aorta, before deploying stent grafts to alleviate pressure on the aneurysm. Previous clinical studies suggests that surgeons continue to struggle with vessel access using standard commercial instruments, prolonging the procedural time and inducing further complications. In this work, we present two contributions to solving this problem: 1) A bespoke 2-segment steerable catheter, consisting of 4 degrees of freedom to enhance dexterity. 2) An expandable, modular tendon-driven actuation platform that can accommodate for the redundancies introduced in our system. To fabricate the catheter, we capitalized on thermal fiber drawing, a technique that creates high-aspect ratio devices at scale, and processed the catheter with laser micro-machining to soften its tip. We evaluated the system using simulations, where we investigated the catheter's bending stiffness, then its steerability with in-vitro experiments in vascular phantoms. This handheld, robotic steerable catheter system has the potential to shorten the length of future endovascular surgeries, and give clinicians the tools to resolve challenging clinical cases.

Alex Ranne, Jinshi Zhao, Ali Anil Demircali +5

Hamlyn Centre for Robotic Surgery, Department of Mechanical Engineering, Imperial College London, SW7 2AZ, UK · Department of Metabolism, Digestion and Reproduction, Imperial College London, SW7 2AZ, UK · Department of Mechanical Engineering, Bursa Uludag University, 16059, Bursa, Turkey +1

Robotic Endovascular Intervention Tendon-Driven Actuation Surgical Robotics

Jul 27, 2026cs.RO

Development of a Handheld Actuation Mechanism for a Tendon-driven Robotically Steered Guidewire

An endovascular intervention begins with a skilled clinician manually navigating a long, slender wire, called a guidewire, to the target location within the vasculature. Due to factors, such as vessel tortuosity and lack of steerability at the guidewire tip, manual navigation of a guidewire could be challenging, potentially resulting in vessel damage, perforation, dissection, and occlusion, as well as postsurgical complications, such as thrombosis. This work details the development of a handheld actuation mechanism for tendon-driven robotic guidewires by utilizing the aforementioned spooling mechanism. Incorporating the compact spooling mechanism into a handheld device enables clinicians to leverage both motorized guidewire steering and manual rotation and translation, if desired. The proposed device is designed such that, while holding the device, the clinician can execute feeding, rotation, and bending motions for up to 1.5 m of the robotically steerable guidewire using a joystick and momentary switch on the handle. Furthermore, the proposed device is demonstrated through navigation in an anatomically accurate phantom aorta model.

Saima Chavenet, Timothy A. Brumfiel, Revanth Konda +1

Medical Robotics and Automation (Robomed) Laboratory · Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology

Robotic Endovascular Intervention Robotics Tendon-Driven Actuation Surgical Robotics

Jun 14, 2026cs.RO

Friction Characterization of a Cable-Driven Differential Actuation System for Lower-Limb Exoskeletons

Lower-limb exoskeletons require actuation systems that can provide accurate joint torque control while preserving low mass and encumbrance. Conventional architectures often rely on independently actuated joints and joint-level torque sensors, increasing system complexity and weight. This paper presents a novel differential actuation architecture for hip-knee flexion/extension, enabling cooperative torque sharing between two motors via a linear differential mapping between motor and joint. To compensate for transmission losses, a model-based friction estimation strategy is developed and experimentally implemented, allowing accurate joint torque estimation without the need for torque sensors. The proposed solution is validated on a physical prototype, demonstrating the feasibility of sensorless torque estimation in a differentially actuated hip-knee module of a lower-limb exoskeleton.

Alberto Maria Nobili, Fabio Salsedo, Alessandro Filippeschi

Institute of Mechanical Intelligence and with the Department of Excellence in Robotics and AI, Sant’Anna School of Advanced Studies, Pisa, Italy · Wearable Robotics s.r.l., Pisa, Italy

Mechanism Design Robotics Assistive Robotics Tendon-Driven Actuation

May 20, 2026cs.RO

WiXus: A Wheeled-Legged Robot with Wire-Driven Environmental Utilizing to Integrate Mobility and Manipulation

Wheeled-legged robots, which have wheels at their feet and achieve high mobility by coordinating wheel drive and leg drive, have been developed. These robots have been developed purely as platforms specialized for locomotion. Therefore, they do not have a means to repurpose their legs for roles other than locomotion, such as object manipulation or tool utilization. In this paper, we address the problem of how to draw out the potential task-execution capability of the legs by freeing them from the roles of locomotion through external body support. To this end, we propose and develop a new robot, WiXus, which fuses a wheeled-legged mechanism with a wire-driven mechanism that utilizes the external environment. The developed WiXus demonstrates not only planar locomotion with wheeled-legged drive, but also three-dimensional mobility such as cliff climbing by coordinating wire-driven and wheeled-legged actuation. Furthermore, by suspending the body with wire-driven actuation, WiXus successfully repurpose its legs as arms to perform object manipulation, (e.g., rescuing a dog (stuffed animal)), and tool utilization (e.g., harvesting an apple (mockup) with loppers). This study demonstrates that the approach of utilizing the environment with wire-driven actuation is a new design principle that extends the operational domain of wheeled-legged robots.

Shintaro Inoue, Kento Kawaharazuka, Temma Suzuki +2

Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

Legged Robotics Robotics Robotic Tool Use Robotic Manipulation Robot Locomotion Tendon-Driven Actuation

May 18, 2026cs.RO

Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots

Continuum robots enable dexterous manipulation in constrained environments, but require accurate and efficient models for real-time manipulation and control. Traditional physics-based models can be computationally expensive and may suffer from inaccuracies due to unmodeled effects, while current learning-based methods often generalize poorly beyond the specific robot on which they are trained. We present a formulation of surrogate modeling for tendon-driven continuum robots as an operator learning problem that maps robot design parameters and tendon actuation inputs to resulting configurations. This formulation enables a single trained model to generalize across a large class of robot designs. We develop four novel neural operator architectures--two based on Deep Operator Networks (DeepONets) and two based on Fourier Neural Operators (FNOs)--and train them on simulation data to predict robot configurations. All architectures achieve good accuracy while allowing for fast and accurate generalization across designs. Our results demonstrate that operator learning provides an effective and generalizable surrogate for continuum robot mechanics in the design space, enabling fast modeling for control, planning, and design optimization in surgical and industrial applications.

Branden Frieden, James M. Ferguson, Alan Kuntz +1

The Robotics Center and the Kahlert School of Computing at the University of Utah, Salt Lake City, UT 84112, USA. · The Departments of Computer Science and Electrical and Computer Engineering at Vanderbilt University, Nashville, TN, 37203, USA.

Neural Surrogate Modeling Fourier Neural Operator Continuum Robot Control Tendon-Driven Actuation Deep Operator Networks Neural Operators

May 18, 2026cs.RO

Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot

Developing dynamic models for tendon-driven continuum robots is challenging due to their nonlinear, high-dimensional, and friction-dominated dynamics. This paper presents a comparative study of data-driven system identification methods, including N4SID, ARX, and SINDYc, for modeling a tendon-actuated continuum robot with rolling joints developed at CERN. Despite the high number of joints of the robot, experimental analysis reveals that a two-degree-of-freedom dynamic model can accurately capture the system dynamics, owing to strong kinematic dependencies between the joints. The models are validated against experimental data, and used in the design of a model predictive controller, demonstrating their feasibility for real-time control.

Harald Minde Hansen, Bjørn Kåre Sæbø, Kristin Y. Pettersen +2

European Organization for Nuclear Research (CERN), Switzerland · Department of Engineering Cybernetics, Norwegian University of Science and Technology, NTNU, NO-7491 Trondheim, Norway

Robot Dynamics Identification Nonlinear System Identification System Identification Continuum Robot Control Tendon-Driven Actuation Soft Robotics

May 16, 2026cs.RO

SSTL: Self-Sensing Tendon Loop for Hysteresis Modeling and Compensation in Tendon-Sheath Mechanisms

Flexible endoscopic robots enable minimally invasive access through natural orifices, but their control accuracy is limited by configuration-dependent hysteresis in the tendon-sheath mechanisms (TSMs). Tendon-sheath friction and tendon elasticity induce a systematic discrepancy between the proximal actuation input and distal output, and this discrepancy varies with the insertion tube configuration. To address this challenge, this paper proposes the Self-Sensing Tendon Loop (SSTL), a double-pass tendon loop routed through the insertion tube and wrapped around a distal pulley, and returned to the proximal end. The loop structure allows both the input and output tensions of the SSTL to be measured proximally, thereby providing an input-output tension profile without requiring distal force or fiber-optic sensors. Because the SSTL shares the same routing path as the actuation TSM, the two TSMs exhibit strongly correlated hysteresis behaviors. From the SSTL tension profile, a learning-based mapping estimates the configuration-dependent hysteresis parameters of the actuation TSM, which are then used by a feedforward controller to compensate for actuation hysteresis. We validate the proposed method by tracking actuation tendon tension under three different insertion tube configurations. Across sinusoidal and random trajectories, the proposed method reduces average RMSE by 88.1% compared with the uncompensated baseline, achieving 97.8% of the performance of direct identification, which requires direct measurement of the input and output tension profile of the actuation TSM.

Myeongbo Park, Junhyun Park, Ihsan Ullah +2

Robotic Control Mechanism Design System Identification Tendon-Driven Actuation Surgical Robotics

May 9, 2026cs.RO

Continuum Robot Modeling with Action Conditioned Flow Matching

Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex tendon routing, compliance, friction, and fabrication variability. In this paper, we address this problem as kinematic self modeling conditioned on action. We present a lightweight 3D printed TDCR hardware platform and an RGB-D data collection pipeline with multiple cameras, and we learn a point cloud flow matching model that maps motor actuation states to the robot's settled 3D geometry. The model is trained from randomly sampled quasi static configurations and evaluated on test motor commands within the same TDCR design family and actuation range. We compare against prior 3D deformable object and robot self modeling approaches in both MuJoCo simulation and real hardware experiments. Experiments on simulated 2-, 3-, and 5-module TDCRs and real 2- and 3-module robots show improved shape prediction accuracy under CD and EMD metrics. We further show in simulation that the same conditional formulation generalizes to tip payload as a conditioning input, enabling payload conditioned steady-state shape prediction. These results demonstrate a data driven self modeling framework for quasi static TDCR geometry prediction.

Jiong Lin, Jinchen Ruan, Hod Lipson

Department of Mechanical Engineering, Columbia University, New York, NY, USA

3D Shape Generation Point Cloud Generation Continuum Robot Control Tendon-Driven Actuation Soft Robotics

Apr 28, 2026cs.RO

Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots

Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian characteristics. Traditional Jacobian-based controllers often struggle with hysteresis-induced oscillations, while conventional learning-based approaches suffer from poor generalization to out-of-distribution trajectories. This paper proposes a reference-augmented offline learning framework for precise 6-DOF tracking control of TDCRs. By leveraging a differentiable RNN-based dynamics surrogate as a gradient bridge, we optimize a control policy through an augmented reference distribution. This multi-scale augmentation scheme incorporates stochastic bias, harmonic perturbations, and random walks, forcing the policy to internalize diverse tracking error recovery mechanisms without additional hardware interaction. Experimental results on a three-section TDCR platform demonstrate that the proposed policy achieves a 50.9% reduction in average position error compared to non-augmented baselines and significantly outperforms Jacobian-based methods in both precision and stability across various speeds. For implementation details and source code, please refer to https://github.com/ZiqingZou/ContinuumControl.

Ziqing Zou, Ke Qiu, Haojian Lu +2

Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

Robot Trajectory Tracking Robot Policy Learning Data Augmentation Continuum Robot Control Tendon-Driven Actuation

Apr 28, 2026cs.RO

Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots

Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control. We develop a GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction to effectively suppress compounding errors during long-horizon auto-regressive prediction. By treating this model as a gradient bridge, an end-to-end neural control policy is optimized through backpropagation, allowing it to implicitly internalize compensation for intricate nonlinearities. Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations. For implementation details and source code, please refer to https://github.com/ZiqingZou/ContinuumControl.

Ziqing Zou, Ke Qiu, Fei Wang +3

Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

Robot Trajectory Tracking Robot Dynamics Learning Recurrent Neural Networks Robust Control Continuum Robot Control Tendon-Driven Actuation+1

Apr 21, 2026cs.RO

A Tendon-Driven Wrist Abduction-Adduction Joint Improves Performance of a 5 DoF Upper Limb Exoskeleton -- Implementation and Experimental Evaluation

Wrist function is essential in performing activities of daily living (ADLs). However, there is limited experimental evidence on the functional impact of wrist Abduction-Adduction (Ab-Ad) joint assistance in upper limb exoskeletons (ULEs) for rehabilitation. This study evaluates the effect of implementing an active wrist Ab-Ad joint in a five degree of freedom (DoF) ULE, EXOTIC2 exoskeleton, to support individuals with severe motor impairments. Methods: A compact, lightweight wrist module with tendon-driven abduction and spring-driven adduction was integrated into the EXOTIC exoskeleton. Eight adults with no motor disabilities completed drinking and scratching tasks under randomized wrist-enabled and wrist-locked conditions along with a preliminary feasibility test in one individual with Amyotrophic lateral sclerosis (ALS). Kinematic and task performance metrics including wrist range of motion, task completion time, spillage and leveling metrics were assessed. Results: Implementing the wrist Ab-Ad DoF improved task success metrics. Spill incidence during the drinking task decreased from 56% to 3%, and leveling success for scratching task improved from 28% to 75%. Conclusion: Integrating wrist Ab-Ad assistance improved key functional task outcomes without increasing execution time. Significance: The study provides the experimental evidence that active wrist Ab-Ad control enhances task-level performance in exoskeleton-assisted ADLs.

Juwairiya S. Khan, Mostafa Mohammadi, Alexander L. Ammitzbøll +8

Neurorehabilitation Robotics and Engineering research group, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Gistrup, Denmark · Department of Materials and Production, Aalborg University, Aalborg East, Denmark · Spinal Cord Injury Centre of Western Denmark, Regional Hospital of Viborg Denmark +3

Assistive Robotics Tendon-Driven Actuation

Apr 21, 2026cs.RO

Design, Modelling and Experimental Evaluation of a Tendon-driven Wrist Abduction-Adduction Mechanism for an upper limb exoskeleton

Wrist exoskeletons play a vital role in rehabilitation and assistive applications, yet conventional actuation mechanisms such as electric motors or pneumatics often introduce undesirable weight, friction, and complexity. This paper presents a novel single-cable (tendon), torsional-spring-assisted actuation mechanism for wrist abduction-adduction, and a simulation-based method for selecting its stiffness parameters. The mechanism employs a single Bowden cable passively tensioned by a spiral torsional spring (clock spring) to maintain continuous cable tension without antagonistic actuation. Kinematic and dynamic modeling of the mechanism was performed to estimate the required torque and identify optimal spring parameters. These simulation-derived parameters guided the design of a functional prototype, which was experimentally evaluated with five participants with no motor disabilities (NMD) under varying arm positions and loading conditions using three spring configurations to account for user variability and modeling uncertainties. Experimental results show consistent agreement with simulation-derived trends, with the nominal spring configuration achieving balanced motion range, torque demand, and repeatability. The results demonstrate that simulation-informed stiffness selection can effectively guide the design of compact, cable-driven wrist exoskeletons while reducing reliance on empirical tuning.

Juwairiya S. Khan, Mostafa Mohammadi, John Rasmussen +1

Neurorehabilitation Robotics and Engineering, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark

Assistive Robotics Tendon-Driven Actuation

Apr 19, 2026cs.RO

MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation

High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand space, increase end-effector mass, and suffer from heat accumulation near the hand. Remote tendon-driven actuation offers an alternative by relocating motors to the robot base or an external motor hub, thereby freeing the fingers and palm for additional degrees of freedom, sensing modules, and maintainable mechanical structures. This paper presents MM-Hand, a 21-DOF Multimodal Modular dexterous hand based on remote tendon-driven actuation. The hand integrates spring-return tendon-driven fingers, modular 3D-printed finger and palm structures, quick tendon connectors for maintenance, and a multimodal sensing system including joint angle sensors, tactile sensors, motor-side feedback, and in-palm stereo vision. We further analyze tendon-sheath length variation and friction loss to guide the design of the routing, motor hub, and closed-loop joint control. Experiments validate the transmission, output force, sensing, and control capability of the system. The fingertip force reaches 25N under a 1m remote sheath transmission, demonstrating practical load capacity despite long-distance tendon routing. Closed-loop joint-level experiments further evaluate command tracking with a static arm and during arm motion. These results show that MM-Hand provides a lightweight, sensor-rich, and maintainable hardware platform for dexterous manipulation research. To support the community, all hardware designs and software frameworks are made fully open-source at https://mmlab.hk/research/MM-Hand.

Zhuoheng Li, Qingquan Lin, Checheng Yu +5

The University of Hong Kong · City University of Hong Kong · XiFengGu Ltd.

Robotic Hand Design Dexterous Manipulation Robotic Manipulation Tendon-Driven Actuation

Apr 18, 2026cs.RO

Time-Division Multiplexing Actuation in Tendon-Driven Arms: Lightweight Design and Fault Tolerance

Robotic manipulators for aerospace applications require a delicate balance between lightweight construction and fault-tolerant operation to satisfy strict weight limitations and ensure reliability in remote, hazardous environments. This paper presents Time-Division Multiplexing Actuation (TDMA), a practical approach for tendon-driven robots that significantly reduces actuator count while preserving high torque output and intrinsic fault tolerance. The key hardware employs a vertically-stacked rotational selection structure that integrates self-rotating TDM motors for rapid configuration, electromagnetic clutches enabling sub-0.1 second engagement, a worm gear reducer for enhanced load capacity and self-locking capability, and a dual-encoder system for precise, long-term positioning. Leveraging TDMA, the proposed MuxArm achieves a self-weight of 2.17 kg, supports an actuator driving capacity of 10 kg, and maintains end-effector accuracy up to 1% of its length, even under partial servo failure. Additionally, an actuation space trajectory planning algorithm is developed, enabling fault-tolerant control and reducing tendon load by up to 50% compared to conventional methods. Comprehensive experiments demonstrate MuxArm's robust performance in diverse settings, including free-space, cluttered, and confined environments.

Shoujie Li, Changqing Guo, Jianle Xu +4

Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639956, Singapore · Department of Automation, Tsinghua University, Beijing 100084, China

Robotic Control Robotics Tendon-Driven Actuation

Mar 19, 2026cs.RO

Tendon-Actuated Robots with a Tapered, Flexible Polymer Backbone: Design, Fabrication, and Modeling

This paper presents the design, modeling, and fabrication of 3D-printed, tendon-actuated continuum robots featuring a flexible, tapered backbone constructed from thermoplastic polyurethane (TPU). Our scalable design incorporates an integrated electronics base housing that enables direct tendon tension control and sensing via actuators and compression load cells. Unlike many continuum robots that are single-purpose and costly, the proposed design prioritizes customizability, rapid assembly, and low cost while enabling high curvature and enhanced distal compliance through geometric tapering, thereby supporting a broad range of compliant robotic inspection and manipulation tasks. We develop a generalized forward kinetostatic model of the tapered backbone based on Cosserat rod theory using a Newtonian approach, extending existing tendon-actuated Cosserat rod formulations to explicitly account for spatially varying backbone cross-sectional geometry. The model captures the graded stiffness profile induced by the tapering and enables systematic exploration of the configuration space as a function of the geometric design parameters. Specifically, we analyze how the backbone taper angle influences the robot's configuration space and manipulability. The model is validated against motion capture data, achieving centimeter-level shape prediction accuracy after calibrating Young's modulus via a line search that minimizes modeling error. We further demonstrate teleoperated grasping using an endoscopic gripper routed along the continuum robot, mounted on a 6-DoF robotic arm. Parameterized iLogic/CAD scripts are provided for rapid geometry generation and scaling. The presented framework establishes a simple, rapid, and reproducible pathway from parametric design to controlled tendon actuation for tapered, tendon-driven continuum robots manufactured using fused deposition modeling 3D printers.

Harald Minde Hansen, Nandita Gallacher, Nicholas B. Andrews +3

Mechatronics, Robotics and Operation Section, European Organization for Nuclear Research (CERN), Meyrin, Switzerland · Department of Aeronautics and Astronautics, University of Washington, Seattle, WA, USA · Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Continuum Robot Control Tendon-Driven Actuation Soft Robotics

Mar 12, 2026cs.RO

CRAFT: A Tendon-Driven Hand with Hybrid Hard-Soft Compliance

We introduce CRAFT hand, a tendon-driven anthropomorphic hand with hybrid hard-soft compliance for contact-rich manipulation. The design is based on a simple idea: contact is not uniform across the hand. Impacts concentrate at joints, while links carry most of the load. CRAFT places soft material at joints and keeps links rigid, and uses rollingcontact joint surfaces to keep flexion on repeatable motion paths. Fifteen motors mounted on the fingers drive the hand through tendons, keeping the form factor compact and the fingers light. In structural tests, CRAFT improves strength and endurance while maintaining comparable repeatability. In teleoperation, CRAFT improves handling of fragile and low-friction items, and the hand covers 33/33 grasps in the Feix taxonomy. The full design costs under $600 and will be released open-source with visionbased teleoperation and simulation integration. Project page: http://craft-hand.github.io/

Leo Lin, Shivansh Patel, Jay Moon +2

University of Illinois Urbana-Champaign · UC Irvine

Robotic Hand Design Robotic Grasping Contact-Rich Robotic Manipulation Robotic Manipulation Tendon-Driven Actuation

Date pendingcs.RO

MuJoCable: Reduced-Order Surface-Routed Cable Transmission for Tendon-Driven Robots

Tendon transmissions reduce distal inertia and add compliance, yet routing, slack, and friction govern motion and force transfer. Mainstream rigid-body robotics simulators such as MuJoCo do not jointly resolve moving noncircular contact, unilateral tension, and segment friction. We present MuJoCable, which adds a reduced-order, configuration-dependent cable transmission to MuJoCo. Its routing algorithm jointly optimizes an ordered path across moving analytic and mesh surfaces. A unilateral axial law, directional Capstan propagation, and nodal virtual work map this path to segment tensions and body forces. The warm-started engine plugin applies these forces during simulation and exposes route and load states for design. Pulley benchmarks recover analytical transmission relations with a Capstan-ratio error below 0.5%. On the underactuated 18-joint SpiRobs, MuJoCable reveals friction-driven load growth and proximal redistribution of joint rotation that the native tendon does not represent. Hardware tests on SpiRobs and a tendon-route-coupled finger reproduce observed motion sequences. By making physical threading executable, MuJoCable brings transmission sources of the simulation-to-reality gap into route, cable, and actuator design before fabrication.

Yi Zhang, Qi Shao, Yicong Lin +3

Robotics Simulation Robotics Tendon-Driven Actuation