Soft Robotics

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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.

Latest papers

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

Feb 20, 2026

Design and Biomechanical Evaluation of a Lightweight Low-Complexity Soft Bilateral Ankle Exoskeleton

Many people could benefit from exoskeleton assistance during gait, for either medical or nonmedical purposes. But exoskeletons bring added mass and structure, which in turn require compensating for. In this work, we present a lightweight, low-complexity, soft bilateral ankle exoskeleton for plantarflexion assistance, with a shoe attachment design that can be mounted on top of any pair of shoes. Experimental tests show no significant difference in lower limb kinematics and kinetics when wearing the exoskeleton in zero-torque mode relative to not wearing an exoskeleton, showing that our device does not obstruct healthy gait, and proving it as a compliant and comfortable device, promising to provide effective assistance. Hence, a control system was developed, and additional tests are underway.

Josée Mallah, Zakii Javed, Zafer Azak +2

Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, United Kingdom · Cambridge University Hospitals NHS Foundation Trust, Cambridge CB22 5LD, United Kingdom

Assistive Robotics Soft Robotics

Feb 20, 2026

Snapping Actuators with Asymmetric and Sequenced Motion

Snapping instabilities in soft structures offer a powerful pathway to achieve rapid and energy-efficient actuation. In this study, an eccentric dome-shaped snapping actuator is developed to generate controllable asymmetric motion through geometry-induced instability. Finite element simulations and experiments reveal consistent asymmetric deformation and the corresponding pressure characteristics. By coupling four snapping actuators in a pneumatic network, a compact quadrupedal robot achieves coordinated wavelike locomotion using only a single pressure input. The robot exhibits frequency-dependent performance with a maximum speed of 72.78~~mm/s at 7.5~~Hz. These findings demonstrate the potential of asymmetric snapping mechanisms for physically controlled actuation and lay the groundwork for fully untethered and efficient soft robotic systems.

Xin Li, Ye Jin, Mohsen Jafarpour +2

Pneumatic Actuation Soft Robotics Soft Pneumatic Actuators

Feb 20, 2026

Ori-Sense: origami capacitive sensing for soft robotic applications

This work introduces Ori-Sense, a compliant capacitive sensor inspired by the inverted Kresling origami pattern. The device translates torsional deformation into measurable capacitance changes, enabling proprioceptive feedback for soft robotic systems. Using dissolvable-core molding, we fabricated a monolithic silicone structure with embedded conductive TPU electrodes, forming an integrated soft capacitor. Mechanical characterization revealed low stiffness and minimal impedance, with torque values below 0.01 N mm for axial displacements between -15 mm and 15 mm, and up to 0.03 N mm at 30 degrees twist under compression. Finite-element simulations confirmed localized stresses along fold lines and validated the measured torque-rotation response. Electrical tests showed consistent capacitance modulation up to 30%, directly correlated with the twist angle, and maximal sensitivity of S_theta ~ 0.0067 pF/deg at 5 mm of axial deformation.

Hugo de Souza Oliveira, Xin Li, Mohsen Jafarpour +1

Soft Robotics Robotic Perception

Feb 20, 2026

Tendon-Driven Reciprocating and Non-Reciprocating Motion via Snapping Metabeams

Snapping beams enable rapid geometric transitions through nonlinear instability, offering an efficient means of generating motion in soft robotic systems. In this study, a tendon-driven mechanism consisting of spiral-based metabeams was developed to exploit this principle for producing both reciprocating and non-reciprocating motion. The snapping structures were fabricated using fused deposition modeling with polylactic acid (PLA) and experimentally tested under different boundary conditions to analyze their nonlinear behavior. The results show that the mechanical characteristics, including critical forces and stability, can be tuned solely by adjusting the boundary constraints. The spiral geometry allows large reversible deformation even when made from a relatively stiff material such as PLA, providing a straightforward design concept for controllable snapping behavior. The developed mechanism was further integrated into a swimming robot, where tendon-driven fins exhibited two distinct actuation modes: reciprocating and non-reciprocating motion. The latter enabled efficient propulsion, producing a forward displacement of about 32 mm per 0.4 s cycle (

\approx

81 mm/s, equivalent to 0.4 body lengths per second). This study highlights the potential of geometry-driven snapping structures for efficient and programmable actuation in soft robotic systems.

Mohsen Jafarpour, Ayberk Yüksek, Shahab Eshghi +2

Department of Functional Morphology and Biomechanics, Zoological Institute, Kiel University, 24118 Kiel, Germany

Soft Robotics Tendon-Driven Actuation

Feb 20, 2026

Design and Characterization of a Dual-DOF Soft Shoulder Exosuit with Volume-Optimized Pneumatic Actuator

Portable pneumatic systems for 2 degree-of-freedom (DOF) soft shoulder exosuits remain underexplored, and face fundamental trade-offs between torque output and dynamic response that are further compounded by the need for multiple actuators to support complex shoulder movement. This work addresses these constraints through a volume-optimized spindle-shaped angled actuator (SSAA) geometry: by reducing actuator volume by 35.7% (357mL vs. 555mL), the SSAA maintains 94.2% of output torque while achieving 35.2% faster dynamic response compared to uniform cylindrical designs. Building on the SSAA, we develop a curved abduction actuator (CAA) based on the SSAA geometry and a horizontal adduction actuator (HAA) based on the pouch motor principle, integrating both into a dual-DOF textile-based shoulder exosuit (390 g). The exosuit delivers multi-modal assistance spanning shoulder abduction, flexion, and horizontal adduction, depending on the actuation. User studies with 10 healthy participants reveal that the exosuit substantially reduces electromyographic (EMG) activity across both shoulder abduction and flexion tasks. For abduction with HAA only, the exosuit achieved up to 59% muscle activity reduction across seven muscles. For flexion, both the single-actuator configuration (HAA only) and the dual-actuator configuration (HAA,+,CAA) reduced EMG activity by up to 63.7% compared to no assistance. However, the incremental benefit of adding the CAA to existing HAA support was limited in healthy users during flexion, with statistically significant additional reductions observed only in pectoralis major. These experimental findings characterize actuator contributions in healthy users and provide design guidance for multi-DOF exosuit systems.

Rui Chen, Domenico Chiaradia, Daniele Leonardis +1

The authors are with the Institute of Mechanical Intelligence and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna (SSSA), 56127 Pisa, Italy

Pneumatic Actuation Upper-Limb Exoskeletons Assistive Robotics Soft Robotics Soft Pneumatic Actuators

Feb 19, 2026

Optically Sensorized Electro-Ribbon Actuator (OS-ERA)

Electro-Ribbon Actuators (ERAs) are lightweight flexural actuators that exhibit ultrahigh displacement and fast movement. However, their embedded sensing relies on capacitive sensors with limited precision, which hinders accurate control. We introduce OS-ERA, an optically sensorized ERA that yields reliable proprioceptive information, and we focus on the design and integration of a sensing solution without affecting actuation. To analyse the complex curvature of an ERA in motion, we design and embed two soft optical waveguide sensors. A classifier is trained to map the sensing signals in order to distinguish eight bending states. We validate our model on six held-out trials and compare it against signals' trajectories learned from training runs. Across all tests, the sensing output signals follow the training manifold, and the predicted sequence mirrors real performance and confirms repeatability. Despite deliberate train-test mismatches in actuation speed, the signal trajectories preserve their shape, and classification remains consistently accurate, demonstrating practical voltage- and speed-invariance. As a result, OS-ERA classifies bending states with high fidelity; it is fast and repeatable, solving a longstanding bottleneck of the ERA, enabling steps toward closed-loop control.

Carolina Gay, Petr Trunin, Diana Cafiso +3

C. Gay, P. Trunin, D. Cafiso and L. Beccai are with the Soft BioRobotics and Perception Lab of the Istituto Italiano di Tecnologia, Via Morego 30 16163, Genova, Italy · Y. Xu and M. Taghavi are with the Department of Bioengineering, Imperial College London, SW7 2BX London, U.K.

Soft Robotics Robot State Estimation

Feb 19, 2026

3D-printed Soft Optical sensor with a Lens (SOLen) for light guidance in mechanosensing

Additive manufacturing is enabling soft robots with increasingly complex geometries, creating a demand for sensing solutions that remain compatible with single-material, one-step fabrication. Optical soft sensors are attractive for monolithic printing, but their performance is often degraded by uncontrolled light propagation (ambient coupling, leakage, scattering), while common miti- gation strategies typically require multimaterial interfaces. Here, we present an approach for 3D printed soft optical sensing (SOLen), in which a printed lens is placed in front of an emitter within a Y-shaped waveguide. The sensing mechanism relies on deformation-induced lens rotation and focal-spot translation, redistributing optical power between the two branches to generate a differential output that encodes both motion direction and amplitude. An acrylate polyurethane resin was modified with lauryl acrylate to improve compliance and optical transmittance, and single-layer optical characterization was used to derive wavelength-dependent refractive index and transmittance while minimizing DLP layer-related artifacts. The measured refractive index was used in simulations to design a lens profile for a target focal distance, which was then printed with sub-millimeter fidelity. Rotational tests demonstrated reproducible branch-selective signal switching over multiple cycles. These results establish a transferable material-to-optics workflow for soft optical sensors with lens with new functionalities for next-generation soft robots

Diana Cafiso, Petr Trunin, Carolina Gay +1

Istituto Italiano di Tecnologia, Soft BioRobotics and Perception, Morego 30, Genoa, 16163, Italy

Feb 19, 2026

Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy

Hybrid rigid-soft robots combine the precision of rigid manipulators with the compliance and adaptability of soft arms, offering a promising approach for versatile grasping in unstructured environments. However, coordinating hybrid robots remains challenging, due to difficulties in modeling, perception, and cross-domain kinematics. In this work, we present a novel augmented reality (AR)-based physical human-robot interaction framework that enables direct teleoperation of a hybrid rigid-soft robot for simple reaching and grasping tasks. Using an AR headset, users can interact with a simulated model of the robotic system integrated into a general-purpose physics engine, which is superimposed on the real system, allowing simulated execution prior to real-world deployment. To ensure consistent behavior between the virtual and physical robots, we introduce a real-to-simulation parameter identification pipeline that leverages the inherent geometric properties of the soft robot, enabling accurate modeling of its static and dynamic behavior as well as the control system's response.

Huishi Huang, Jack Klusmann, Haozhe Wang +9

Advanced Robotics Centre (ARC), National University of Singapore (NUS), 117608, Singapore. · Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 138632, Singapore. · Chair of Cognitive Systems, Technical University of Munich (TUM), 80333, Munich. +2

Human-Robot Interaction Robotic Grasping Robot Teleoperation Soft Robotics

Feb 18, 2026

Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped

SLOT (Soft Legged Omnidirectional Tetrapod), a tendon-driven soft quadruped robot with 3D-printed TPU legs, is presented to study physics-informed modeling and control of compliant legged locomotion using only four actuators. Each leg is modeled as a deformable continuum using discrete Cosserat rod theory, enabling the capture of large bending deformations, distributed elasticity, tendon actuation, and ground contact interactions. A modular whole-body modeling framework is introduced, in which compliant leg dynamics are represented through physically consistent reaction forces applied to a rigid torso, providing a scalable interface between continuum soft limbs and rigid-body locomotion dynamics. This formulation allows efficient whole-body simulation and real-time control without sacrificing physical fidelity. The proposed model is embedded into a convex model predictive control framework that optimizes ground reaction forces over a 0.495 s prediction horizon and maps them to tendon actuation through a physics-informed force-angle relationship. The resulting controller achieves asymptotic stability under diverse perturbations. The framework is experimentally validated on a physical prototype during crawling and walking gaits, achieving high accuracy with less than 5 mm RMSE in center of mass trajectories. These results demonstrate a generalizable approach for integrating continuum soft legs into model-based locomotion control, advancing scalable and reusable modeling and control methods for soft quadruped robots.

Saumya Karan, Neerav Maram, Suraj Borate +1

IIT Gandhinagar, Department of Electrical Engineering, Gujarat, India · Sreenidhi Institute of Science and Technology, Department of Electronics and Computer Engineering, Telangana, India

Quadruped Locomotion Soft Robotics Continuum Robot Control Model Predictive Control

Feb 18, 2026

Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

Skeletal muscle-based biohybrid actuators have proved to be a promising component in soft robotics, offering efficient movement. However, their intrinsic biological variability and nonlinearity pose significant challenges for controllability and predictability. To address these issues, this study investigates the application of supervised learning, a form of machine learning, to model and predict the behavior of biohybrid machines (BHMs), focusing on a muscle ring anchored on flexible polymer pillars. First, static prediction models (i.e., random forest and neural network regressors) are trained to estimate the maximum exerted force achieved from input variables such as muscle sample, electrical stimulation parameters, and baseline exerted force. Second, a dynamic modeling framework, based on Long Short-Term Memory networks, is developed to serve as a digital twin, replicating the time series of exerted forces observed in response to electrical stimulation. Both modeling approaches demonstrate high predictive accuracy. The best performance of the static models is characterized by R2 of 0.9425, whereas the dynamic model achieves R2 of 0.9956. The static models can enable optimization of muscle actuator performance for targeted applications and required force outcomes, while the dynamic model provides a foundation for developing robustly adaptive control strategies in future biohybrid robotic systems.

Michail-Antisthenis Tsompanas, Marco Perez Hernandez, Faisal Abdul-Fattah +9

Unconventional Computing Laboratory & School of Computing & Creative Technologies, College of Arts, Technology and Environment, University of the West of England, Bristol, BS16 1QY, United Kingdom · School of Computing & Creative Technologies, College of Arts, Technology and Environment, University of the West of England, Bristol, BS16 1QY, United Kingdom · Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute for Science and Technology (BIST), Barcelona, Spain +3

Robotics Soft Robotics

Feb 16, 2026

A Soft Wrist with Anisotropic and Selectable Stiffness for Robust Robot Learning in Contact-rich Manipulation

Contact-rich manipulation tasks in unstructured environments pose significant robustness challenges for robot learning, where unexpected collisions can cause damage and hinder policy acquisition. Existing soft end-effectors face fundamental limitations: they either provide a limited deformation range, lack directional stiffness control, or require complex actuation systems that compromise practicality. This study introduces CLAW (Compliant Leaf-spring Anisotropic soft Wrist), a novel soft wrist mechanism that addresses these limitations through a simple yet effective design using two orthogonal leaf springs and rotary joints with a locking mechanism. CLAW provides large 6-degree-of-freedom deformation (40mm lateral, 20mm vertical), anisotropic stiffness that is tunable across three distinct modes, while maintaining lightweight construction (330g) at low cost ($550). Experimental evaluations using imitation learning demonstrate that CLAW achieves 76% success rate in benchmark peg-insertion tasks, outperforming both the Fin Ray gripper (43%) and rigid gripper alternatives (36%). CLAW successfully handles diverse contact-rich scenarios, including precision assembly with tight tolerances and delicate object manipulation, demonstrating its potential to enable robust robot learning in contact-rich domains. Project page: https://project-page-manager.github.io/CLAW/

Steven Oh, Tomoya Takahashi, Cristian C. Beltran-Hernandez +2

OMRON SINIC X Corp., Bunkyo-ku, Tokyo, 113-0033, Japan.

Soft Robotics Robotic Manipulation Contact-Rich Robotic Manipulation

Feb 3, 2026

Soft Swing-up: Benchmarking Model-Based Optimal Control for Rigid-Soft Underactuated Systems

Continuum soft robots are inherently underactuated and subject to intrinsic input constraints, making dynamic control particularly challenging, especially in hybrid rigid-soft robots. While most existing methods focus on quasi-static behaviors, dynamic tasks such as swing-up require accurate exploitation of continuum dynamics. This has led to studies on simple low-order template systems that often fail to capture the complexity of real continuum deformations. Model-based optimal control offers a systematic solution; however, its application to rigid-soft robots is often limited by the computational cost and inaccuracy of numerical differentiation for high-dimensional models. Building on recent advances in the Geometric Variable Strain model that enable analytical derivatives, this work investigates three optimal control strategies for underactuated soft systems -- Direct Collocation, Differential Dynamic Programming, and Nonlinear Model Predictive Control -- to perform dynamic swing-up tasks. To address stiff continuum dynamics and constrained actuation, implicit integration schemes and warm-start strategies are employed to improve numerical robustness and computational efficiency. The methods are evaluated in simulation on three Rigid-Soft and high-order soft benchmark systems -- the Soft Cart-Pole, the Soft Pendubot, and the Soft Furuta Pendulum -- highlighting their performance and computational trade-offs.

Daniele Caradonna, Nikhil Nair, Anup Teejo Mathew +5

The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy · Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy · Department of Cognitive Robotics, Delft University of Technology, 2628 CN Delft, The Netherlands +3

Robotic Control Model Predictive Control Continuum Robot Control Optimal Control Soft Robotics

Jan 27, 2026

Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist

Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-based control to enable safe and robust manipulation. The soft wrist allows safe contact exploration during data collection, while tactile memory reuses past demonstrations via retrieval for flexible adaptation to unseen scenarios. The core of this system is the Masked Tactile Trajectory Transformer (MAT

^\text{3}

), which jointly models spatiotemporal interactions between robot actions, distributed tactile cues, force-torque measurements, and proprioceptive signals. Through masked token prediction, MAT

^\text{3}

learns rich spatiotemporal representations by inferring missing sensory information from context, autonomously extracting task-relevant features without explicit subtask segmentation. We validate our approach on peg-in-hole tasks with diverse pegs and conditions in real-robot experiments. Our extensive evaluation demonstrates that MAT

^\text{3}

achieves higher success rates than the baselines over all conditions and shows remarkable capability to adapt to unseen pegs and conditions.

Tatsuya Kamijo, Mai Nishimura, Nodoka Shibasaki +3

OMRON SINIC X Corporation, 5-24-5 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan · The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan · Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA

Robotic Control Dexterous Manipulation Contact-Rich Robotic Manipulation Generative Modeling Tactile Sensing for Robotic Manipulation Soft Robotics

Jan 20, 2026

Highly Deformable Proprioceptive Membrane for Real-Time 3D Shape Reconstruction

Reconstructing the three-dimensional (3D) geometry of object surfaces is essential for robot perception, yet vision-based approaches degrade under low illumination or occlusion. This limitation motivates the design of a proprioceptive membrane that conforms to the surface of interest and infers 3D geometry by reconstructing its own deformation. Conventional deformation-aware membranes typically rely on resistive, capacitive, or magneto-sensitive mechanisms, but can suffer from structural complexity, limited compliance during large-scale deformation, and susceptibility to electromagnetic interference. This work presents a soft, flexible, and stretchable proprioceptive silicone membrane based on optical waveguide sensing. The membrane integrates edge-mounted LEDs and centrally-distributed photodiodes (PDs) within a multilayer elastomeric composite. Rich deformation-dependent light-intensity signals are decoded by a data-driven model to recover the membrane geometry. Real-time reconstruction is demonstrated on a customized 140 mm square membrane at an end-to-end update rate of 90 Hz, achieving an average reconstruction error of 1.307 mm for out-of-plane deformation of up to 25 mm. The proposed sensor also demonstrates accurate reconstruction under large in-plane deformation, achieving reliable shape recovery up to 75% strain with an average Chamfer distance of 1.214 mm. The proposed framework provides a scalable, robust, and low-profile solution for global shape perception in deformable robotic systems.

Guanyu Xu, Jiaqi Wang, Dezhong Tong +1

University of Michigan, Ann Arbor, MI 48109, USA

Tactile Sensing 3D Reconstruction Deformable Shape Reconstruction Robotic Tactile Sensing Soft Robotics

Nov 24, 2025

Soft Pneumatic Grippers: Topology optimization, 3D-printing and Experimental validation

Typically, heuristic/trial-based approaches are used to design soft pneumatic grippers (SPGs). This paper presents a systematic topology optimization framework for developing SPGs. The design-dependent nature of actuating load is modeled using Darcy's law with an added drainage term. A 2D soft arm unit is then optimized as a compliant mechanism under pneumatic loading. To ensure the design is robust and manufacturable, the problem is formulated as a min-max optimization, where output deformations of blueprint and eroded designs are considered. A volume constraint is imposed on the blueprint part, while a strain-energy constraint is enforced on the eroded part. The Method of Moving Asymptotes is employed to solve optimization problems. The optimized 2D part is extruded suitably to generate a 3D unit. Ten such 3D units are assembled to create a gripper arm. Both the optimized 2D unit and the corresponding gripper arm outperform their conventional rectangular designs under pneumatic loading, demonstrating the efficacy of the proposed approach. The arms are fabricated using the SLA printing technique. Numerical and experimental results are compared at different pneumatic loads. Four 3D-printed arms are integrated with a supporting structure to form the SPG. The gripping action of the SPG is demonstrated on objects with different weights, sizes, structures, stiffnesses, and shapes.

Prabhat Kumar, Chandra Prakash, Josh Pinskier +2

Department Mechanical and Aerospace Engineering. India Institute of Technology Hyderbad, Telangana 502285, India · CSIRO Robotics, Pullenvale, QLD 4069, Australia · Faculty of Mechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628CD, Zuid-Holland, The Netherlands

Topology Optimization Mechanism Design Soft Robotics Soft Pneumatic Actuators

Nov 15, 2025

Multilaminate piezoelectric polymer actuators for insect-scale softrobots

Piezoelectric ceramics enable fast, precise, and powerful actuation with integrated sensing in insect-scale robots. However, they are brittle, failing at low strains limiting its applications involving high impacts, unexpected loading, or soft robotics. To address this limitation, we investigate polyvinylidene fluoride (PVDF) as a compliant alternative: it tolerates substantially larger strains, but its low piezoelectric coefficient limits the output of conventional single-layer actuators. We demonstrate a novel manufacturing process for multilayer PVDF actuators that utilizes off the shelf materials and provides parallel bussing of signal voltage to reduce integration complexity. Using first principles models validated with experiments, we show a tradeoff between force (linear or superlinear increase with layer count) and displacement (cubic decrease with layer count) for the actuator geometry chosen. Our work positions PVDF actuation an optimization problem for future robotic applications with clear design knobs available to meet performance demands. Further, we show that using thinner PVDF layers within a stack increases the specific power density of the actuators making them ideal for size, weight, and power (SWAP) limitations in small scale robotics. Among the actuators fabricated, >9 mm of free deflection, >20 mN of blocked force, and >500 Hz bandwidth were achieved. To illustrate their potential in future robust robotic platforms, we integrate our actuators into a low-profile insect-scale robot that leverages resonance to achieve planar locomotion while being robust to large external impacts.

Nicholas Gunter, Heiko Kabutz, Alexander Hedrick +1

Animal Inspired Movement and Robotics Laboratory, Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder

Robotics Soft Robotics

Nov 10, 2025

Rapidly Learning Soft Robot Control via Implicit Time-Stepping

With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies. In contrast, soft robotic simulation frameworks remain scarce and are seldom adopted by the soft robotics community. This gap stems partly from the lack of easy-to-use, general-purpose frameworks and partly from the high computational cost of accurately simulating continuum mechanics, which often renders policy learning infeasible. In this work, we demonstrate that rapid soft robot policy learning is indeed achievable via implicit time-stepping. Our simulator of choice, DisMech, is a general-purpose, fully implicit soft-body simulator capable of handling both soft dynamics and frictional contact. We further introduce delta natural curvature control, a method analogous to delta joint position control in rigid manipulators, providing an intuitive and effective means of enacting control for soft robot learning. To highlight the benefits of implicit time-stepping and delta curvature control, we conduct extensive comparisons across four diverse soft manipulator tasks against one of the most widely used soft-body frameworks, Elastica. With implicit time-stepping, parallel stepping of 500 environments achieves up to 6x faster speeds for non-contact cases and up to 40x faster for contact-rich scenarios. Finally, a comprehensive sim-to-sim gap evaluation--training policies in one simulator and evaluating them in another--demonstrates that implicit time-stepping provides a rare free lunch: dramatic speedups achieved without sacrificing accuracy.

Andrew Choi, Dezhong Tong, Xiaonan Huang

Horizon Robotics, Cupertino, CA, USA · Robotics Department at the University of Michigan, Ann Arbor, MI, USA

Robot Policy Learning Robotics Simulation Continuum Robot Control Soft Robotics Deformable Object Simulation

Nov 9, 2025

External Photoreflective Tactile Sensing Based on Surface Deformation Measurement

We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surface deformation of silicone skin to estimate contact force without embedding tactile transducers. Locating the sensor off the contact interface reduces damage risk, preserves softness, and simplifies fabrication and maintenance. We first characterize the optical sensing element and the compliant skin, thendetermine the design of a prototype tactile sensor. Compression experiments validate the approach, exhibiting a monotonic force output relationship consistent with theory, low hysteresis, high repeatability over repeated cycles, and small response indentation speeds. We further demonstrate integration on a soft robotic gripper, where the module reliably detects grasp events. Compared with liquid filled or wireembedded tactile skins, the proposed modular add on architecture enhances durability, reduces wiring complexity, and supports straightforward deployment across diverse robot geometries. Because the sensing principle reads skin strain patterns, it also suggests extensions to other somatosensory cues such as joint angle or actuator state estimation from surface deformation. Overall, leveraging surface compliance with an external optical module provides a practical and robust route to equip soft robots with force perception while preserving structural flexibility and manufacturability, paving the way for robotic applications and safe human robot collaboration.

Seiichi Yamamoto, Hiroki Ishizuka, Takumi Kawasetsu +6

Graduate School of Engineering Science, The University of Osaka, Toyonaka 560-0043, Japan

Tactile Sensing Contact Force Estimation Tactile Sensing for Robotic Manipulation Robotic Tactile Sensing Soft Robotics

Nov 4, 2025

A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms

This paper presents a quantitative comparison between centralised and distributed multi-agent reinforcement learning (MARL) architectures for controlling a soft robotic arm modelled as a Cosserat rod in simulation. Using PyElastica and the OpenAI Gym interface, we train both a global Proximal Policy Optimisation (PPO) controller and a Multi-Agent PPO (MAPPO) under identical budgets. Both approaches are based on the arm having

n

number of controlled sections. The study systematically varies

n

and evaluates the performance of the arm to reach a fixed target in three scenarios: default baseline condition, recovery from external disturbance, and adaptation to actuator failure. Quantitative metrics used for the evaluation are mean action magnitude, mean final distance, mean episode length, and success rate. The results show that there are no significant benefits of the distributed policy when the number of controlled sections

n\le4

. In very simple systems, when

n\le2

, the centralised policy outperforms the distributed one. When

n

increases to

4< n\le 12

, the distributed policy shows a high sample efficiency. In these systems, distributed policy promotes a stronger success rate, resilience, and robustness under local observability and yields faster convergence given the same sample size. However, centralised policies achieve much higher time efficiency during training as it takes much less time to train the same size of samples. These findings highlight the trade-offs between centralised and distributed policy in reinforcement learning-based control for soft robotic systems and provide actionable design guidance for future sim-to-real transfer in soft rod-like manipulators.

Linxin Hou, Qirui Wu, Zhihang Qin +3

Department of Electrical and Computer Engineering, National University of Singapore · Department of Computer Science, National University of Singapore · Department of Mechanical Engineering, National University of Singapore

RL Control Robotic Policy Evaluation Decentralized MARL Soft Robotics Multi-Agent Reinforcement Learning

Oct 13, 2025

Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation

Knee rehabilitation plays a critical role in restoring patients' mobility and functional independence. Traditional rigid rehabilitation exoskeletons are often bulky and cumbersome to wear, whereas soft pneumatic exoskeletons offer lightweight, wearable, and intrinsically compliant solutions that are better suited for human--robot interaction. However, achieving precise motion control for soft exoskeletons remains challenging due to the difficulty of accurately modeling pneumatic actuators and the patient-specific human--robot coupled dynamics during rehabilitation training. To address these challenges, this paper proposes a Koopman-based Model Predictive Control (KMPC) framework for soft knee rehabilitation exoskeletons. The nonlinear human--robot coupled system is represented through a lifted linear Koopman model, enabling predictive control with explicit handling of constraints. In addition to actuation commands used to control valves and pumps, electromyography (EMG) signals are incorporated as system inputs, allowing the Koopman model to capture voluntary neuromuscular contribution and individual neuromuscular characteristics. Experimental results on both healthy participants and patients demonstrate that the proposed framework improves model prediction accuracy and effectively captures subject-specific behaviors, thereby supporting EMG-informed subject-specific assistance within the tested seated knee-rehabilitation setting. Compared with conventional Proportional--Integral--Derivative (PID) control, the proposed KMPC approach achieves lower tracking errors and reduced actuation effort in both passive and active rehabilitation modes. Additional comparisons with Iterative Learning Control (ILC) further validate the tracking performance of the proposed controller.

Junxiang Wang, Han Zhang, Zehao Wang +3

School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China · Seventh Affiliated Hospital, SunYat-sen University, Shenzhen 510275, China

Robotic Control Model Predictive Control Assistive Robotics Soft Robotics

Apr 20, 2025

Soft yet Effective Robots via Holistic Co-Design

Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Despite progress, the field struggles to balance task-specific performance with broader factors like durability and manufacturability--a difficulty that we find is compounded by traditional sequential design processes with their lack of feedback loops. In this perspective, we review emerging co-design approaches that simultaneously optimize the soft robot's body and brain, enabling the discovery of unconventional designs highly tailored to the given tasks. Their adoption is limited by narrow objectives, gaps between simulated and real performance, and computational cost. To address these challenges, we propose a holistic co-design framework that incorporates a broader range of design values, integrates real-world prototyping to refine evaluations, and boosts efficiency through surrogate metrics and model-based control strategies. Finally, we outline research priorities in design priors and metrics, AI-assisted evaluation, and balancing computational refinement with physical testing and safety with performance.

Maximilian Stölzle, Niccolò Pagliarani, Francesco Stella +6

Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. · Cognitive Robotics, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands. · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. +4

Hardware-Software Co-Design Soft Robotics