Soft Robotics

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Overview

Soft robotics investigates how deformable machines can move, manipulate objects, and assist people while adapting to contact and irregular surroundings. Common designs use pneumatic or hydraulic chambers, tendons, dielectric elastomers, and magnetic or shape-changing structures; models range from Cosserat rods and finite-element methods to learned dynamics. Control approaches include model-predictive control, reinforcement learning, visual servoing, and tactile or proprioceptive feedback, evaluated in tasks such as grasping, locomotion, wearable assistance, and medical navigation. A central challenge is balancing accurate, efficient modeling and control with the changing shapes, hysteresis, manufacturing variation, and sim-to-real gaps that make soft robots adaptable in the first place.

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231 papers14 papers in the last four weeks, down 53% on the four weeks before. 0.1% of all new papers.

Mar 9, 2026

Unified Structural-Hydrodynamic Modeling of Underwater Underactuated Mechanisms and Soft Robots

Underwater robots are widely deployed for ocean exploration and manipulation. Underactuated mechanisms are advantageous in aquatic environments because reducing actuator count lowers motor-leakage risk while introducing inherent mechanical compliance. However, accurate modeling of underwater underactuated and soft robotic systems remains challenging, as it requires identifying high-dimensional structural and hydrodynamic parameters. In this work, we propose a trajectory-driven global optimization framework for unified structural-hydrodynamic modeling of underwater multibody systems. Inspired by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed approach simultaneously identifies coupled elastic, damping, and distributed hydrodynamic parameters through trajectory-level matching between simulated and experimental motion. This enables high-fidelity reproduction of underactuated mechanisms and compliant soft robotic systems in underwater environments, using as little as a single video recording. We first validate the framework on a link-by-link underactuated multibody mechanism, demonstrating accurate identification of distributed hydrodynamic coefficients, with normalized end-effector position error below 5% across multiple trajectories, initial conditions, and both active-passive and fully passive configurations. The modeling strategy is further validated on an asymmetric octopus-inspired soft arm, confirming its effectiveness for compliant soft robotic systems. Finally, eight identified arms are assembled into a swimming octopus robot, where the unified parameter set enables realistic whole-body behavior without additional retuning. These results demonstrate the scalability and transferability of the proposed structural-hydrodynamic modeling framework across underwater underactuated and soft robotic systems.

Chenrui Zhang, Yiyuan Zhang, Yunfei Ye +3

Department of Mechanical Engineering and Advanced Robotics Centre, National University of Singapore, Singapore. · Singapore-MIT Alliance for Research and Technology (SMART) Centre, 138602, Singapore.

Robot System IdentificationUnderwater RoboticsSystem IdentificationSoft Robotics
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Mar 8, 2026

Cable-driven Continuum Robotics: Proprioception via Proximal-integrated Force Sensing

Micro-scale continuum robots face significant limitations in achieving three-dimensional contact force perception, primarily due to structural miniaturization, nonlinear mechanical, and sensor integration. To overcome these limitations, this paper introduces a novel proprioception method for cable-driven continuum robots based on proximal-integrated force sensing (i.e., cable tension and six-axis force/torque (F/T) sensor), inspired by the tendon-joint collaborative sensing mechanism of the finger. By integrating biomechanically inspired design principles with nonlinear modeling, the proposed method addresses the challenge of force perception (including the three-dimensional contact force and the location of the contact point) and shape estimation in micro-scale continuum robots. First, a quasi-bionic mapping between human tissues/organs and robot components is established, enabling the transfer of the integrated sensing strategy of tendons, joints, and neural feedback to the robotic system. Second, a multimodal perception strategy is developed based on the structural constraints inherent to continuum robots. The complex relationships among mechanical and material nonlinearities, robot motion states, and contact forces are formulated as an optimization problem to reduce the perception complexity. Finally, experimental validation demonstrates the effectiveness of the proposed method. This work lays the foundation for developing safer and smarter continuum robots, enabling broader clinical adoption in complex environments.

Gang Zhang, Junyan Yan, Jibiao Chen +1

Department of Mechanical and Automation Engineering and T Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong · Curve Robotics Limited, Hong Kong · Shun Hing Institute of Advanced Engineering and Multi-Scale Medical Robotics Center, Hong Kong

Contact Force EstimationContinuum Robot Shape EstimationRobotic PerceptionTactile SensingSoft Robotics
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Mar 7, 2026

Soft Rigid Hybrid Gripper with Inflatable Silicone Pockets for Tunable Frictional Grasping

Grasping objects with diverse mechanical properties, such as heavy, slippery, or fragile items, remains a significant challenge in robotics. Conventional rigid grippers typically rely on increasing the normal forces to secure an object, however, this can cause damage to fragile objects due to excessive force. To address this limitation, we propose a soft rigid hybrid gripper finger that combines rigid structural shells with soft, inflatable silicone pockets, which could be integrated into a conventional gripper. The hybrid gripper can actively modulate its surface friction by varying the internal air pressure of the silicone pockets, enabling the gripper to securely grasp objects without increasing the gripping force. This is demonstrated by fundamental experimental results, in which an increase in internal pressure leads to a proportional increase in the effective coefficient of friction. The gripping experiments also show that the integrated gripper can stably lift heavy and slippery objects or fragile, deformable objects, such as eggs, tofu, fruits, and paper cups, with minimal damage by increasing friction rather than applying high force.

Hoang Hiep Ly, Cong-Nhat Nguyen, Doan-Quang Tran +6

Robotic GraspingRobotic Hand DesignSoft Robotics
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Mar 7, 2026

VSL-Skin: Individually Addressable Phase-Change Voxel Skin for Variable-Stiffness and Virtual Joints Bridging Soft and Rigid Robots

Soft robots are compliant but often cannot support loads or hold their shape, while rigid robots provide structural strength but are less adaptable. Existing variable-stiffness systems usually operate at the scale of whole segments or patches, which limits precise control over stiffness distribution and virtual joint placement. This paper presents the Variable Stiffness Lattice Skin (VSL-Skin), the first system to enable individually addressable voxel-level morphological control with centimeter-scale precision. The system provides three main capabilities: nearly two orders of magnitude stiffness modulation across axial (15-1200 N/mm), shear (45-850 N/mm), bending (810^2 - 310^4 N/deg), and torsional modes with centimeter-scale spatial control; the first demonstrated 30% axial compression in phase-change systems while maintaining structural integrity; and autonomous component-level self-repair through thermal cycling, which eliminates fatigue accumulation and enables programmable sacrificial joints for predictable failure management. Selective voxel activation creates six canonical virtual joint types with programmable compliance while preserving structural integrity in non-activated regions. The platform incorporates closed-form design models and finite element analysis for predictive synthesis of stiffness patterns and joint placement. Experimental validation demonstrates 30% axial contraction, thermal switching in 75-second cycles, and cut-to-fit integration that preserves addressability after trimming. The row-column architecture enables platform-agnostic deployment across diverse robotic systems without specialized infrastructure. This framework establishes morphological intelligence as an engineerable system property and advances autonomous reconfigurable robotics.

Zihan Oliver Zeng, Jiajun An, Preston Luk +1

School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA. · Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA. · School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA.

RoboticsSoft Robotics
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Mar 6, 2026

A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots

Soft robots achieve functionality through tight coupling among geometry, material composition, and actuation. As a result, effective design optimization requires these three aspects to be considered jointly rather than in isolation. This coupling is computationally challenging: nonlinear large-deformation mechanics increase simulation cost, while contact, collision handling, and non-smooth state transitions limit the applicability of standard gradient-based approaches. We introduce a smooth, low-dimensional design embedding for soft robots that unifies shape morphing, multi-material distribution, and actuation within a single structured parameter space. Shape variation is modeled through continuous deformation maps of a reference geometry, while material properties are encoded as spatial fields. Both are constructed from shared basis functions. This representation enables expressive co-design while drastically reducing the dimensionality of the search space. In our experiments, we show that design expressiveness increases with the number of basis functions, unlike comparable neural network encodings whose representational capacity does not scale predictably with parameter count. We further show that joint co-optimization of shape, material, and actuation using our unified embedding consistently outperforms sequential strategies. All experiments are performed independently of the underlying simulator, confirming compatibility with black-box simulation pipelines. Across multiple dynamic tasks, the proposed embedding surpasses neural network and voxel-based baseline parameterizations while using significantly fewer design parameters. Together, these findings demonstrate that structuring the design space itself enables efficient co-design of soft robots.

Vittorio Candiello, Manuel Mekkattu, Mike Y. Michelis +1

Soft Robotics Lab, D-MAVT, ETH Zurich, Switzerland. · ETH AI Center, ETH Zurich, Switzerland.

Soft RoboticsRobot Morphology-Control Co-Design
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Mar 5, 2026

Control Lyapunov Functions for Underactuated Soft Robots

Soft and soft-rigid hybrid robots are inherently underactuated and operate under tight actuator limits, making task-space control with stability guarantees challenging. Common nonlinear strategies for soft robots (e.g., those based on PD control) often rely on the assumption of full actuation with no actuator limits. This paper presents a general control framework for task-space regulation and tracking of underactuated soft robots under bounded inputs. The method enforces a rapidly exponentially stabilizing control Lyapunov function as a convex inequality constraint while simultaneously satisfying underactuated full-body dynamics and actuator bounds. We validate the approach in simulation on several platforms spanning increasing underactuation: a simple two link tendon-driven "finger", a trimmed helicoid manipulator, and a highly underactuated spiral robot. We compare against a number of baseline methods from the literature. Results show improved task-space accuracy and consistent Lyapunov convergence under input limits, achieving superior set-point and trajectory-tracking performance.

Huy Pham, Zach J. Patterson

Mechanical and Aerospace Engineering, Case Western Reserve University

Robotic ControlSoft RoboticsLyapunov-Based ControlRobot Trajectory Tracking
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Mar 5, 2026

Observer Design for Augmented Reality-based Teleoperation of Soft Robots

Although virtual and augmented reality are gaining traction as teleoperation tools for various types of robots, including manipulators and mobile robots, they are not being used for soft robots. The inherent difficulties of modelling soft robots mean that combining accurate and computationally efficient representations is very challenging. This paper presents an augmented reality interface for teleoperating these devices. The developed system consists of Microsoft HoloLens 2 glasses and a central computer responsible for calculations. Validation is performed on PETER, a highly modular pneumatic manipulator. Using data collected from sensors, the computer estimates the robot's position based on the physics of the virtual reality programme. Errors obtained are on the order of 5% of the robot's length, demonstrating that augmented reality facilitates operator interaction with soft manipulators and can be integrated into the control loop.

Jorge Francisco García-Samartín, Iago López Pérez, Emirhan Yolcu +2

Centro de Automática y Robótica, Universidad Politécnica de Madrid, c/ José Gutiérrez Abascal, nº 2, 28006, Madrid, España. · Middle East Technical University, Dumlupinar Blv No 1, 06800 Cankaya, Ankara, Türkiye.

Robot TeleoperationSoft RoboticsRobot State EstimationAugmented Reality
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Mar 5, 2026

Data-Driven Control of a Magnetically Actuated Fish-Like Robot

Magnetically actuated fish-like robots offer promising solutions for underwater exploration due to their miniaturization and agility; however, precise control remains a significant challenge because of nonlinear fluid dynamics, flexible fin hysteresis, and the variable-duration control steps inherent to the actuation mechanism. This paper proposes a comprehensive data-driven control framework to address these complexities without relying on analytical modeling. Our methodology comprises three core components: 1) developing a forward dynamics model (FDM) using a neural network trained on real-world experimental data to capture state transitions under varying time steps; 2) integrating this FDM into a gradient-based model predictive control (G-MPC) architecture to optimize control inputs for path following; and 3) applying imitation learning to approximate the G-MPC policy, thereby reducing the computational cost for real-time implementation. We validate the approach through simulations utilizing the identified dynamics model. The results demonstrate that the G-MPC framework achieves accurate path convergence with minimal root mean square error (RMSE), and the imitation learning controller (ILC) effectively replicates this performance. This study highlights the potential of data-driven control strategies for the precise navigation of miniature, fish-like soft robots.

Akiyuki Koyama, Hiroaki Kawashima

Graduate School of Information Science, University of Hyogo, Japan

Robotic ControlUnderwater RoboticsSoft RoboticsModel Predictive ControlRobot Dynamics Learning
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Mar 4, 2026

A Soft Robotic Demonstration in the Stratosphere

Machines designed for operation in Space, as well as other extreme environments, need to be both resilient and adaptable when mission parameters change. Soft robots offer advantages in adaptability, but most lack resilience to the pressure and temperature extremes found as close as the Stratosphere. Dielectric elastomer actuators overcome some of those limitations when built as solid state compliant capacitors capable of converting electrical energy into mechanical work, but the elastomer resilience limits the device's operating window. Here we present a crosslinking mechanism for silicone elastomers under ultraviolet light using trimethyl(methylcyclopentadienyl)platinum(IV) as a catalyst to react hydrosilane to vinyl groups. The formation of carbon-carbon bonds enables fast processing under UV light and exceptional electro-mechanical performance in dielectric elastomer actuators. The material resilience advantage is demonstrated in controlled experiments at -40° and 120° C, as well as near vacuum, in comparison with state-of-the-art acrylic and silicone chemistries. Fully autonomous systems controlling grippers made with the novel silicone were integrated into payloads for high altitude balloon testing. Two stratospheric balloon missions were carried out and demonstrated DEAs as a viable soft robotic technology under space-like conditions (as high as 23.6 km elevation, at <0.05 atm and -55° C). The combinations of chemical building blocks and catalyst can be further expanded to address other challenges for silicones, including adhesion and additive manufacturing.

Codrin Tugui, Tirth Thakar, Anatol Gogoj +5

University of Connecticut, School of Mechanical, Aerospace, and Manufacturing Engineering, Storrs, Connecticut USA · Institute of Macromolecular Chemistry Petru Poni; Iași, Romania · University of Toronto, Department of Mechanical and Industrial Engineering, Toronto, Ontario, Canada

Soft RoboticsMaterials ScienceSpace Robotics
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Mar 4, 2026

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

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.

Yiyao Yue, Noah Barnes, Lingyun Di +4

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 InterventionContinuum Robot ControlSoft RoboticsKoopman Operator Learning
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Mar 4, 2026

Characterization and Correlation of Robotic Snake Scale Friction and Locomotion Speed

Snake robots are inspired by the ability of biological snakes to move over rock, grass, leaves, soil, up trees, along pavement and more. Their ability to move in multiple distinct environments is due to their legless locomotion strategy, which combines distinct gaits with a skin that exhibits frictional anisotropy. Designing soft robotic snakes with similar capabilities requires an understanding of how this underlying frictional anisotropy should be created in engineered systems, and how variances in the frictional anisotropy ratio affect locomotion speed and direction on different surfaces. While forward and backward frictional ratios have been characterized for previous scale designs, lateral friction and the associated ratios are often overlooked. In this paper, our contributions include: (i) the development of a novel articulated pseudo-skin design that is modular, easy to construct and has removable or replaceable scales; (ii) experimental measurement of the frictional characteristics of otherwise-identical scales at varying angles of attack (15°, 25°, 35°, 45°) on different surfaces of interest (grass, bark, smooth surface, carpet);(iii) separate measurements of locomotion speed for each angle and surface. Consequently, while we observed some consistent trends between frictional coefficients and scale angle, aligning with literature and intuition, we were not able to consistently identify expected correlations between frictional ratios and locomotion speed. We conclude that either frictional ratios alone are not sufficient to predict the observed speed of a snake robot, or that specific measurement approaches are required to accurately capture these ratios.

Umit Sen, Andri Mahegan, Gina Olson

Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, USA.

Robot LocomotionSoft Robotics
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Mar 4, 2026

Soft Semi-active Back Support Device with Adaptive Force Profiles using Variable-elastic Actuation and Weight Feedback

Portable active back support devices (BSDs) offer tunable assistance but are often bulky and heavy, limiting their usability. In contrast, passive BSDs are lightweight and compact but lack the ability to adapt their assistance to different back movements. We present a soft, lightweight, and compact BSD that combines a variable-stiffness passive element and an active element (an artificial muscle) in parallel. The device provides tunable assistance through discrete changes in stiffness values and active force levels. We validate the device's tuning capabilities through bench testing and on-body characterization. Further, we use the device's tuning capabilities to provide weight-adaptive object lifting and lowering assistance. We detect the weight handled by the user based on forearm force myography and upper-back inertial measurement unit data. Furthermore, electromyography analyses in five participants performing symmetric object lifting and lowering tasks showed reductions in back extensor activity. Preliminary results in one participant also indicated reduced muscle activity during asymmetric lifting.

Rohan Khatavkar, The Bach Nguyen, Inseung Kang +2

Arizona State University · Carnegie Mellon University

Assistive RoboticsSoft Robotics
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Mar 3, 2026

Passive Phase-Oriented Impedance Shaping for Rapid Acceleration in Soft Robotic Swimmers

Rapid acceleration and burst maneuvers in underwater robots depend less on maintaining precise resonance and more on force--velocity phase alignment during thrust generation. In this work, we investigate constrained-layer damping (CLD) as a passive mechanism for frequency-selective impedance shaping in soft robotic swimmers. Unlike conventional stiffness-tuning approaches, CLD selectively amplifies the dissipative component of bending impedance while preserving storage stiffness, passively shifting the impedance composition toward dissipative dominance as actuation frequency increases. We characterize this behavior through dry impedance measurements, demonstrate that CLD enhances thrust and alters force--motion phase relationships across Strouhal numbers in constrained propulsion tests, and validate that passive impedance shaping yields a nearly five-fold increase in peak acceleration and a three-fold increase in terminal velocity in unconstrained swimming trials. These results establish phase-oriented passive impedance modulation as a simple, control-free pathway for improving transient propulsion in soft robotic systems.

Qimin Feng, Orion A. Roberts, Qiang Zhong

Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.

Robot LocomotionUnderwater RoboticsSoft RoboticsImpedance ControlStructural Mechanics
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Mar 3, 2026

Instant and Reversible Adhesive-free Bonding Between Silicones and Glossy Papers for Soft Robotics

Integrating silicone with non-extensible materials is a common strategy used in the fabrication of fluidically-driven soft actuators, yet conventional approaches often rely on irreversible adhesives or embedding processes that are labor-intensive and difficult to modify. This work presents silicone-glossy paper bonding (SGB), a rapid, adhesive-free, and solvent-reversible bonding approach that forms robust silicone-paper interfaces simply through contact. The SGB interface withstands high mechanical loads (shear strength > 61 kPa) and can be fully detached and reassembled via ethanol immersion without loss of performance, enabling component reuse and rapid redesign. Characterization studies indicate that surface functional groups primarily govern adhesion on the glossy paper and the modulus of the silicone, while durability and environmental response clarify the conditions for reversible debonding. The results further suggest a synergistic interaction of hydrogen bonding and oligomer diffusion, yielding strong yet reconfigurable adhesion. Soft actuators fabricated using SGB design exhibit equal or greater performance compared to conventional embedded-layer design and enable programmable actuation modes, including contraction, bending, and twisting. By simplifying fabrication while supporting reuse and rapid iteration, SGB offers a scalable and sustainable platform for rapid prototyping in soft robotics.

Takumi Shibuya, Kazuya Murakami, Akitsu Shigetou +1

Department of Mechanical and Intelligent Systems Engineering, The University of Electro-communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan. · Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan.

Materials ScienceSoft Robotics
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Mar 2, 2026

A Novel Modular Cable-Driven Soft Robotic Arm with Multi-Segment Reconfigurability

This paper presents a novel, modular, cable-driven soft robotic arm featuring multi-segment reconfigurability. The proposed architecture enables a stackable system with independent segment control, allowing scalable adaptation to diverse structural and application requirements. The system is fabricated from soft silicone material and incorporates embedded tendon-routing channels with a protective dual-helical tendon structure. Experimental results showed that modular stacking substantially expanded the reachable workspace: relative to the single-segment arm, the three-segment configuration achieved up to a 13-fold increase in planar workspace area and a 38.9-fold increase in workspace volume. Furthermore, this study investigated the effect of silicone stiffness on actuator performance. The results revealed a clear trade-off between compliance and stiffness: softer silicone improved bending flexibility, while stiffer silicone improved structural rigidity and load-bearing stability. These results highlight the potential of stiffness tuning to balance compliance and strength for configuring scalable, reconfigurable soft robotic arms.

Moeen Ul Islam, Cheng Ouyang, Xinda Qi +3

Department of Agricultural and Biological Engineering, Mississippi State University, MS, USA · Department of Electrical and Computer Engineering, Michigan State University, MI, USA · Texas A&M AgriLife Research and Extension Center in Dallas, Texas A&M University, TX, USA

Soft RoboticsModular RoboticsTendon-Driven Actuation
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Mar 1, 2026

A Deployable Bio-inspired Compliant Leg Design for Enhanced Leaping in Quadruped Robots

Quadruped robots are becoming increasingly essential for various applications, including industrial inspection and catastrophe search and rescue. These scenarios require robots to possess enhanced agility and obstacle-navigation skills. Nonetheless, the performance of current platforms is often constrained by insufficient peak motor power, limiting their ability to perform explosive jumps. To address this challenge, this paper proposes a bio-inspired method that emulates the energy-storage mechanism found in froghopper legs. We designed a Deployable Compliant Leg (DCL) utilizing a specialized 3D-printed elastic material, Polyether block amide (PEBA), featuring a lightweight internal lattice structure. This structure functions analogously to biological tendons, storing elastic energy during the robot's squatting phase and rapidly releasing it to augment motor output during the leap. The proposed mechanical design significantly enhances the robot's vertical jumping capability. Through finite element analysis (FEA) and experimental validation, we demonstrate a relative performance improvement of 17.1% in vertical jumping height.

Yiyang Chen, Yuxin Liu, Jinzheng Zhou +4

Department of Mechatronics and Robotics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China. · Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States.

Bioinspired RoboticsRoboticsQuadruped LocomotionSoft Robotics
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Feb 28, 2026

TMR-VLA:Vision-Language-Action Model for Magnetic Motion Control of Tri-leg Silicone-based Soft Robot

In-vivo environments, magnetically actuated soft robots offer advantages such as wireless operation and precise control, showing promising potential for painless detection and therapeutic procedures. We developed a trileg magnetically driven soft robot (TMR) whose multi-legged design enables more flexible gaits and diverse motion patterns. For the silicone made of reconfigurable soft robots, its navigation ability can be separated into sequential motions, namely squatting, rotation, lifting a leg, walking and so on. Its motion and behavior depend on its bending shapes. To bridge motion type description and specific low-level voltage control, we introduced TMR-VLA, an end-to-end multi-modal system for a trileg magnetic soft robot capable of performing hybrid motion types, which is promising for developing a navigation ability by adapting its shape to language-constrained motion types. The TMR-VLA deploys embodied endoluminal localization ability from EndoVLA, and fuses sequential frames and natural language commands as input. Low-level voltage output is generated based on the current observation state and specific motion type description. The result shows the TMR-VLA can predict how the voltage applied to TMR will change the dynamics of a silicon-made soft robot. The TMR-VLA reached a 74% average success rate.

Ruijie Tang, Chi Kit Ng, Kaixuan Wu +5

Language-Conditioned Robot ControlSoft RoboticsVLMs for Robotics
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Feb 27, 2026

Evaluating Accuracy of Vine Robot Shape Sensing with Distributed Inertial Measurement Units

Soft, tip-extending vine robots are well suited for navigating tight, debris-filled environments, making them ideal for urban search and rescue. Sensing the full shape of a vine robot's body is helpful both for localizing information from other sensors placed along the robot body and for determining the robot's configuration within the space being explored. Prior approaches have localized vine robot tips using a single inertial measurement unit (IMU) combined with force sensing or length estimation, while one method demonstrated full-body shape sensing using distributed IMUs on a passively steered robot in controlled maze environments. However, the accuracy of distributed IMU-based shape sensing under active steering, varying robot lengths, and different sensor spacings has not been systematically quantified. In this work, we experimentally evaluate the accuracy of vine robot shape sensing using distributed IMUs along the robot body. We quantify IMU drift, measuring an average orientation drift rate of 1.33 degrees/min across 15 sensors. For passive steering, mean tip position error was 11% of robot length. For active steering, mean tip position error increased to 16%. During growth experiments across lengths from 30-175 cm, mean tip error was 8%, with a positive trend with increasing length. We also analyze the influence of sensor spacing and observe that intermediate spacings can minimize error for single-curvature shapes. These results demonstrate the feasibility of distributed IMU-based shape sensing for vine robots while highlighting key limitations and opportunities for improved modeling and algorithmic integration for field deployment.

Alexis E. Laudenslager, Antonio Alvarez Valdivia, Nathaniel Hanson +1

Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, Indiana, USA. · Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts, USA

Continuum Robot Shape EstimationRobot State EstimationSoft Robotics
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Feb 27, 2026

Geometry-based pneumatic actuators for soft robotics

Soft pneumatic actuators enable safe human-machine interaction with lightweight and powerful applied parts. On the other side, they suffer design limitations as regards complex actuation patterns, including minimum bending radii, multi-states capabilities and structural stability. We present geometry-based pneumatic actuators (GPAs), a design and implementation approach that introduces constraint layers with configurable CNC heat-sealed chambers. The approach achieves predictable deformation, near-zero bending radii, multi-states actuation, and enables customizable and repeatable complex actuated geometries. Mathematical modeling reveals predictable linear angle transformations and validates nonlinear torque-angle relationships across diverse configurations. We demonstrate versatility of the GPAs approach through three applications: a 49 g wrist exoskeleton reducing muscle activity by up to 51%, a 30.8 g haptic interface delivering 8 N force feedback with fast response, and a 208 g bipedal robot achieving multi-gait locomotion. GPAs establish a configurable platform for next-generation wearable robotics, haptic systems, and soft locomotion devices.

Rui Chen, Daniele Leonardis, Domenico Chiaradia +1

Institute of Mechanical Intelligence, School of Advanced Studies Sant’Anna (SSSA), 56127 Pisa, Italy.

Soft Pneumatic ActuatorsPneumatic ActuationRoboticsSoft Robotics
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Feb 27, 2026

Tilt-X: Enabling Compliant Aerial Manipulation through a Tiltable-Extensible Continuum Manipulator

Aerial manipulators extend the reach and manipulation capabilities of uncrewed multirotor aerial vehicles for inspection, agriculture, sampling, and delivery. Continuum arm aerial manipulation systems offer lightweight, dexterous, and compliant interaction opportunities. Existing designs allow manipulation only below the UAV which restricts their deployability in multiple directions and through clutter. They are also sensitive to propeller downwash. Addressing these limitations, we present Tilt-X, a continuum arm aerial manipulator that integrates a tilting mechanism, a telescopic stage, and a cable-driven continuum section. We present its design and kinematic model and validate it through flight demonstrations. Tilt-X enables a volumetric workspace with up to 75 mm extension and planar orientations between 0∘^\circ to 90∘^\circ. Experiments comparing end effector pose with and without downwash quantitatively measure its accuracy, providing critical evidence to guide the design and control of reliable aerial manipulators. Results show stabilisation of end effector pose as the manipulator extends out of the propeller influence zone.

Anuraj Uthayasooriyan, Krishna Manaswi Digumarti, Jack Breward +3

QUT Centre for Robotics, Queensland University of Technology, Brisbane, QLD Australia 4000 · School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD Australia 4000 · REF,Queensland University of Technology, Brisbane, QLD Australia 4000

Aerial ManipulationAerial RoboticsRobotic ManipulationSoft Robotics
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Feb 26, 2026

Printed helicoids with embedded air channels make sensorized segments for soft continuum robots

Soft robots enable safe, adaptive interaction with complex environments but remain difficult to sense and control due to their highly deformable structures. Architected soft materials such as helicoid lattices offer tunable stiffness and strength but are challenging to instrument because of their sparse geometry. We introduce a fabrication method for embedding air channels into helicoid-based soft continuum robots. Multi-material segments fabricated via vision-controlled jetting in a single print interface with PCBs housing miniature pressure sensors and IMUs for distributed deformation sensing. We characterize the mechanical properties of four helicoid designs and validate the sensor response to fundamental deformation modes. To demonstrate the platform's scalability, we construct and mechanically evaluate a meter-scale, 14-DoF cable-driven soft arm capable of open-loop trajectory tracking and object grasping, with tactile-based stiffness detection demonstrated using the gripper sensors. This approach establishes a scalable fabrication strategy for sensorized architected materials in large-scale soft robotic systems.

Annan Zhang, Hanna Matusik, Miguel Flores-Acton +3

All authors are with the MIT Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA.

Soft RoboticsMaterials Science
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Feb 26, 2026

Simple Models, Real Swimming: Digital Twins for Tendon-Driven Underwater Robots

Mimicking the graceful motion of swimming animals remains a core challenge in soft robotics due to the complexity of fluid-structure interaction and the difficulty of controlling soft, biomimetic bodies. Existing modeling approaches are often computationally expensive and impractical for complex control or reinforcement learning needed for realistic motions to emerge in robotic systems. In this work, we present a tendon-driven fish robot modeled in an efficient underwater swimmer environment using a simplified, stateless hydrodynamics formulation implemented in the widespread robotics framework MuJoCo. With just two real-world swimming trajectories, we identify five fluid parameters that allow a matching to experimental behavior and generalize across a range of actuation frequencies. We show that this stateless fluid model can generalize to unseen actuation and outperform classical analytical models such as the elongated body theory. This simulation environment runs faster than real-time and can easily enable downstream learning algorithms such as reinforcement learning for target tracking, reaching a 93% success rate. Due to the simplicity and ease of use of the model and our open-source simulation environment, our results show that even simple, stateless models -- when carefully matched to physical data -- can serve as effective digital twins for soft underwater robots, opening up new directions for scalable learning and control in aquatic environments.

Mike Y. Michelis, Nana Obayashi, Josie Hughes +1

Soft Robotics Lab and ETH AI Center, ETH Zurich, Switzerland · Prema Lab, New York University, USA · CREATE Lab, EPFL, Switzerland

Underwater RoboticsRobotics SimulationDigital TwinsSoft RoboticsRobot Dynamics Identification
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Feb 24, 2026

Unified Complementarity-Based Contact Modeling and Planning for Soft Robots

Soft robots were introduced in large part to enable safe, adaptive interaction with the environment, and this interaction relies fundamentally on contact. However, modeling and planning contact-rich interactions for soft robots remain challenging: dense contact candidates along the body create redundant constraints and rank-deficient LCPs, while the disparity between high stiffness and low friction introduces severe ill-conditioning. Existing approaches rely on problem-specific approximations or penalty-based treatments. This letter presents a unified complementarity-based framework for soft-robot contact modeling and planning that brings contact modeling, manipulation, and planning into a unified, physically consistent formulation. We develop a robust Linear Complementarity Problem (LCP) model tailored to discretized soft robots and address these challenges with a three-stage conditioning pipeline: inertial rank selection to remove redundant contacts, Ruiz equilibration to correct scale disparity and ill-conditioning, and lightweight Tikhonov regularization on normal blocks. Building on the same formulation, we introduce a kinematically guided warm-start strategy that enables dynamic trajectory optimization through contact using Mathematical Programs with Complementarity Constraints (MPCC) and demonstrate its effectiveness on contact-rich ball manipulation tasks. In conclusion, CUSP provides a new foundation for unifying contact modeling, simulation, and planning in soft robotics.

Milad Azizkhani, Yue Chen

Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology, Atlanta, GA, USA · Coulter Department of Biomedical Engineering (Georgia Tech and Emory University), Georgia Institute of Technology, Atlanta, GA, USA

Soft RoboticsContact-Rich Robotic ManipulationConstrained OptimizationTrajectory Optimization
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Feb 24, 2026

Surface-based Manipulation Using Tunable Compliant Porous-Elastic Soft Sensing

There is a growing need for soft robotic platforms that perform gentle, precise handling of a wide variety of objects. Existing surface-based manipulation systems, however, lack the compliance and tactile feedback needed for delicate handling. This work introduces the COmpliant Porous-Elastic Soft Sensing (COPESS) integrated with inductive sensors for adaptive object manipulation and localised sensing. The design features a tunable lattice layer that simultaneously modulates mechanical compliance and sensing performance. By adjusting lattice geometry, both stiffness and sensor response can be tailored to handle objects with varying mechanical properties. Experiments demonstrate that by easily adjusting one parameter, the lattice density, from 7 % to 20 %, it is possible to significantly alter the sensitivity and operational force range (about -23x and 9x, respectively). This approach establishes a blueprint for creating adaptive, sensorized surfaces where mechanical and sensory properties are co-optimized, enabling passive, yet programmable, delicate manipulation.

Gayatri Indukumar, Muhammad Awais, Diana Cafiso +2

Soft BioRobotics Perception Group at Istituto Italiano di Tecnologia, 16163, Genova, Italy · University of Genoa, 16126, Genova, Italy · Biorobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy +1

Robotic ManipulationSoft RoboticsRobotic Tactile SensingTactile Sensing
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Feb 24, 2026

A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators

Short lifetime under high electrical fields hinders the widespread robotic application of linear dielectric elastomer actuators (DEAs). Systematic scanning is difficult due to time-consuming per-sample testing and the high-dimensional parameter space affecting performance. To address this, we propose an optimization pipeline enabled by a novel testing robot capable of scanning DEA lifetime. The robot integrates electro-mechanical property measurement, programmable voltage input, and multi-channel testing capacity. Using it, we scanned the lifetime of Elastosil-based linear actuators across parameters including input voltage magnitude, frequency, electrode material concentration, and electrical connection filler. The optimal parameter combinations improved operational lifetime under boundary operating conditions by up to 100% and were subsequently scaled up to achieve higher force and displacement output. The final product demonstrated resilience on a modular, scalable quadruped walking robot with payload carrying capacity (>100% of its untethered body weight, and >700% of combined actuator weight). This work is the first to introduce a self-driving lab approach into robotic actuator design.

Ang Li, Alexander Yin, Alexander White +6

Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8 Canada · Institute of Materials Science, University of Connecticut, Storrs, CT, 06269 U.S.A. · School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT, 06269 U.S.A. +2

Soft RoboticsAutonomous ExperimentationRobotic Data Collection
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Feb 23, 2026

Hydrodynamic Performance Enhancement of Unmanned Underwater Gliders with Soft Robotic Morphing Wings for Agility Improvement

This work assesses the hydrodynamic efficiency of Underwater Unmanned Vehicles (UUVs) equipped with soft morphing wings compared to conventional rigid wings. Unlike rigid wings, deformable counterparts can alter their aerodynamic properties on demand. Improvements in hydrodynamic efficiency extend a UUV's operational range and may determine mission feasibility. Structural and Computational Fluid Dynamics (CFD) simulations were conducted for both a soft morphing wing and a UUV incorporating it. The results show that a UUV employing soft wings achieves 9.75 percent higher overall efficiency than an equivalent vehicle with traditional rigid wings. These findings confirm the potential of soft robotics to enhance underwater vehicle performance, particularly in applications requiring pressure-agnostic operation.

A. Giordano, G. De Meurichy, V. Telazzi +6

Laboratory of Sustainability Robotics, Empa, 8600 Dübendorf, Switzerland. · Department of Aeronautics, Imperial College London, SW7 2AZ London, United Kingdom. · Laboratory for Computational Engineering, Empa, 8600 Dübendorf, Switzerland. +2

Underwater RoboticsSoft Robotics
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Feb 23, 2026

TactiVerse: Generalizing Multi-Point Tactile Sensing in Soft Robotics Using Single-Point Data

Real-time prediction of deformation in highly compliant soft materials remains a significant challenge in soft robotics. While vision-based soft tactile sensors can track internal marker displacements, learning-based models for 3D contact estimation heavily depend on their training datasets, inherently limiting their ability to generalize to complex scenarios such as multi-point sensing. To address this limitation, we introduce TactiVerse, a U-Net-based framework that formulates contact geometry estimation as a spatial heatmap prediction task. Even when trained exclusively on a limited dataset of single-point indentations, our architecture achieves highly accurate single-point sensing, yielding a superior mean absolute error of 0.0589 mm compared to the 0.0612 mm of a conventional regression-based CNN baseline. Furthermore, we demonstrate that augmenting the training dataset with multi-point contact data substantially enhances the sensor's multi-point sensing capabilities, significantly improving the overall mean MAE for two-point discrimination from 1.214 mm to 0.383 mm. By successfully extrapolating complex contact geometries from fundamental interactions, this methodology unlocks advanced multi-point and large-area shape sensing. Ultimately, it significantly streamlines the development of marker-based soft sensors, offering a highly scalable solution for real-world tactile perception.

Junhui Lee, Hyosung Kim, Saekwang Nam

Graduate School of Data Science at Kyungpook National University, 41566 Daegu, Republic of Korea · Major in Data Science Convergence

Tactile SensingSoft RoboticsVision-Based Tactile Sensing
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Feb 22, 2026

3D Shape Control of Extensible Multi-Section Soft Continuum Robots via Visual Servoing

In this paper, we propose a novel vision-based control algorithm for regulating the whole body shape of extensible multisection soft continuum manipulators. Contrary to existing vision-based control algorithms in the literature that regulate the robot's end effector pose, our proposed control algorithm regulates the robot's whole body configuration, enabling us to leverage its kinematic redundancy. Additionally, our model-based 2.5D shape visual servoing provides globally stable asymptotic convergence in the robot's 3D workspace compared to the closest works in the literature that report local minima. Unlike existing visual servoing algorithms in the literature, our approach does not require information from proprioceptive sensors, making it suitable for continuum manipulators without such capabilities. Instead, robot state is estimated from images acquired by an external camera that observes the robot's whole body shape and is also utilized to close the shape control loop. Traditionally, visual servoing schemes require an image of the robot at its reference pose to generate the reference features. In this work, we utilize an inverse kinematics solver to generate reference features for the desired robot configuration and do not require images of the robot at the reference. Experiments are performed on a multisection continuum manipulator demonstrating the controller's capability to regulate the robot's whole body shape while precisely positioning the robot's end effector. Results validate our controller's ability to regulate the shape of continuum robots while demonstrating a smooth transient response and a steady-state error within 1 mm. Proof-of-concept object manipulation experiments including stacking, pouring, and pulling tasks are performed to demonstrate our controller's applicability.

Abhinav Gandhi, Shou-Shan Chiang, Cagdas D. Onal +1

Robotics Engineering Department, Worcester Polytechnic Institute 100 Institute Rd, Worcester, MA 01609, USA

Continuum Robot ControlVision-Based Robot ControlSoft Robotics
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Feb 20, 2026

Robotic Fruits with Tunable Stiffness and Sensing: Towards a Methodology for Developing Realistic Physical Twins of Fruits

The global agri-food sector faces increasing challenges from labour shortages, high consumer demand, and supply-chain disruptions, resulting in substantial losses of unharvested produce. Robotic harvesting has emerged as a promising alternative; however, evaluating and training soft grippers for delicate fruits remains difficult due to the highly variable mechanical properties of natural produce. This makes it difficult to establish reliable benchmarks or data-driven control strategies. Existing testing practices rely on large quantities of real fruit to capture this variability, leading to inefficiency, higher costs, and waste. The methodology presented in this work aims to address these limitations by developing tunable soft physical twins that emulate the stiffness characteristics of real fruits at different ripeness levels. A fiber-reinforced pneumatic physical twin of a kiwi fruit was designed and fabricated to replicate the stiffness at different ripeness levels. Experimental results show that the stiffness of the physical twin can be tuned accurately over multiple trials (97.35 - 99.43% accuracy). Gripping tasks with a commercial robotic gripper showed that sensor feedback from the physical twin can reflect the applied gripping forces. Finally, a stress test was performed over 50 cycles showed reliable maintenance of desired stiffness (0.56 - 1.10% error). This work shows promise that robotic physical twins could adjust their stiffness to resemble that of real fruits. This can provide a sustainable, controllable platform for benchmarking and training robotic grippers.

Saitarun Nadipineni, Keshav Pandiyan, Kaspar Althoefer +2

School of Engineering and Materials Science, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4NS, United Kingdom. · Department of Robotics, Ritsumeikan University, Japan

Soft RoboticsAgricultural RoboticsRobot Manipulation BenchmarksRobotic Grasping
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Feb 20, 2026

Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators

For fully actuated rigid robots, kinematic inversion is a purely geometric problem, efficiently solved by closed-loop inverse kinematics (CLIK) schemes that compute joint velocities to position the robot body in space. For underactuated soft robots, however, not all configurations are attainable through control action, making kinematic inversion extremely ill-posed. Extensions of CLIK address this by introducing end-to-end mappings from actuation to task space for the controller to operate on, but typically assume finite dimensions of the underlying virtual configuration space. In this work, we formulate CLIK in the infinite-dimensional domain to reason about the entire soft robot shape while solving tasks, thereby skipping intermediate approximation steps. We do this by composing an actuation-to-shape map with a shape-to-task map, deriving the differential end-to-end kinematics via an infinite-dimensional chain rule, and thereby obtaining a Jacobian-based CLIK algorithm. Since complex soft-robot models are rarely available in closed form, and neither the actuation-to-shape mapping nor its Jacobian can typically be obtained analytically, we propose to learn it using differentiable neural operator networks. We first present an analytical study on a constant-curvature segment, and then apply the neural version of the algorithm to a three-fiber soft robotic arm whose underlying model relies on morphoelasticity and active filament theory.

Carina Veil, Moritz Flaschel, Ellen Kuhl +1

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

Inverse KinematicsContinuum Robot ControlSoft Robotics
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