Recent advances in machine learning have begun to embed oscillatory network principles within neural architectures, aiming to enhance computational efficiency and robustness in time-series regression. Building on these developments, we take a step toward applying such principles to the learning of physical dynamics. We introduce Neural Linear Oscillator Networks (nLON): a vision-based framework that extracts compact latent representations of complex motion directly from image sequences. A convolutional autoencoder encodes position and velocity into a low-dimensional manifold, whose temporal evolution is governed by coupled linear mechanical oscillators driven by a linear combination of the inputs. This strong structural prior not only promotes sample efficiency and interpretability but also guarantees that the learned model remains a mechanical, Wiener-type system. From this formulation, we derive closed-loop controllers that ensure stable regulation. We focus on soft robots-systems whose nonlinear, continuous, and high-dimensional dynamics make them both a challenging and ideal testbed for our approach. Using tentacle robots in high-fidelity simulations and real-world experiments, we validate that our framework delivers accurate long-horizon predictions and consistently surpasses state-of-the-art baselines, achieving superior structural fidelity and final-step accuracy. Finally, we leverage the learned dynamics for model-based control, demonstrating in simulation that the resulting scheme achieves robust and reliable tracking.

Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivates the need for automatic methods that extract task-specific, interpretable low-dimensional dynamics directly from data. We propose a methodology that combines a linear autoencoder with symbolic regression to derive such models. The linear autoencoder provides a consistent latent embedding for configurations, velocities, accelerations, and inputs, enabling the sparse identification of nonlinear dynamics (SINDy) to operate in a compact, physics-aligned space. A multi-phase, hybrid-aware training scheme ensures coherent latent coordinates across contact transitions. We focus our validation on quadruped jumping—a representative, challenging, yet contained scenario in which a principled template model is especially valuable. The resulting symbolic dynamics outperform the state-of-the-art handcrafted actuated spring-loaded inverted pendulum (aSLIP) baseline in simulation and hardware across multiple robots and jumping modalities.

Soft robots' ability to safely navigate complex environments motivates the development of algorithms for accurate environmental interaction assessment, enabling greater autonomy. Specifically, strain-based shape and force estimation of continuum robots with embedded soft sensors poses an open challenge mainly owing to continuous softness, anisotropic deformation, and non-linear properties. Mathematical description of deformable soft bodies and accurate estimation of external forces are crucial for achieving controllable and intelligent behaviors of these robots. In this paper, a kinetostatic strain-based modeling for rod-driven soft robots (RDSR) with embedded stretch sensors is proposed, which incorporates local strains, actuation variables, and external interactions. The strain model enables full shape estimation of the robot and prediction of strain variations in soft bodies. Building on this, we develop a force estimator based on predicted and measured sensor and actuator lengths to evaluate 3D external forces, accounting for both orthogonal and tangential components relative to the backbone. Moreover, we introduce a methodology using a novel ellipsoid representation to handle tangential forces that may become insensitive in certain singular configurations. This estimator allows us to either disregard such forces when they do not influence deformation or estimate them when they become observable. Our simulations and experiments demonstrate how this approach can be used to analyze the robot's configuration and successfully estimate external forces. Finally, it is demonstrated that when the continuum arm follows trajectories with higher strain sensitivity, tangential force estimation is significantly improved.

Soft robots, with their compliant and underactuated nature, pose significant challenges for real-time shape regulation. Practical implementations of these methods often rely on fully-actuated approximations, over-looking the underactuated nature of these continuum structures. This study experimentally validates model-based controllers through collocated control that explicitly address underactuation, incorporating gravity cancellation and elasticity compensation to outperform conventional PD/PID approaches. A new multi-segment soft robot with a passively actuated segment has been designed, enabling experimental validation and providing strong evidence of the controllers’ effectiveness. The work bridges theory and practice, offering a practical framework for real-time shape regulation applicable to diverse soft robotic systems.

Segmenting gas bubbles in multiphase flows is a critical yet unsolved challenge in numerous industrial settings, from metallurgical processing to maritime drag reduction. Traditional approaches — and most recent learning-based methods — assume near-spherical shapes, limiting their effectiveness in regimes where bubbles undergo deformation, coalescence, or breakup. This complexity is particularly evident in air lubrication systems, where coalesced bubbles form amorphous and topologically diverse patches. In this work, we revisit the problem through the lens of modern vision foundation models. We cast the task as a transfer learning problem and demonstrate, for the first time, that a fine-tuned Segment Anything Model (SAM v2.1) can accurately segment highly non-convex, irregular bubble structures using as few as 100 annotated images.

Modeling and predicting the motion of soft robots remains challenging due to their infinite-dimensional and highly nonlinear dynamics. A promising direction is to learn dynamics directly from high-dimensional sensory streams. Yet, standard RGB cameras suffer from motion blur, making it challenging to capture fast transients and biasing learning toward steadystate behaviors. Here, we turn to event-based cameras, which provide asynchronous, high-frequency visual information better suited to capturing dynamic deformations. We propose a learning architecture that encodes two-channel event frames from a DVS sensor through a convolutional autoencoder while jointly learning a compact latent representation of the robot's dynamics. Within this space, we test several latent models, including a novel spiking-harmonic latent oscillator network (snLON), in which spiking neurons capture the event structure of the data stream and drive a latent Oscillator Network that represents the underlying mechanical dynamics. Validated in both simulation and real-world experiments, the proposed framework predicts long-horizon soft-robot motion with high accuracy and consistency from a single initial event frame and control sequence.

Industrial robotics demands significant energy to operate, making energy-reduction methodologies increasingly important. Strategies for planning minimum-energy trajectories typically involve solving nonlinear optimal control problems (OCPs), which rarely cope with real-time (RT) requirements. In this article, we propose a paradigm for generating near minimum-energy trajectories for manipulators by learning from optimal solutions. Our paradigm leverages a residual learning approach, which embeds boundary conditions (BCs) while focusing on learning only the adjustments needed to steer a standard solution to an optimal one. Compared to a computationally expensive OCP-based planner, our paradigm achieves 87.3% of the performance near the training dataset and 50.8% far from the dataset, while being two to three orders-of-magnitude faster.

Breastfeeding is essential for infant nutrition, but the increasing number of women returning to work before weaning highlights the need for efficient and comfortable milk expression methods. Traditional breast pumps rely solely on vacuum suction, which can cause discomfort, tissue damage, and longer extraction times compared to natural nursing. This study aims to develop a breast pump that better mimics the biomechanics of infant breastfeeding to improve comfort and efficiency. We investigated two actuator designs–membrane and soft pleated–integrated into the breast shield to replicate infant sucking. The pleated actuator proved most effective, offering a wide range of expansion and contraction. Unlike traditional pumps, vacuum is applied through radial expansion, allowing the nipple to widen rather than elongate, closely simulating infant tongue movements. The breast shield was fabricated using additive manufacturing with soft, elastic materials, enabling complex geometries and varied stiffness. The prototype was tested against a commercial pump using an artificial breast phantom. Results suggest this design can enhance milk output, reduce pumping time, and improve user comfort. By merging soft robotics with biological insights, our approach offers a promising alternative to conventional breast pumps.

Soft robots enable safe and adaptive interaction with their environment, but still face major challenges in detecting contact during manipulation. This paper presents a low-cost vision-based setup designed to obtain groundtruth contact areas of soft manipulators without requiring the embedding of sensors into their structure. The platform uses an external camera and controlled lighting to capture the contact interface under different materials and configurations. Three segmentation methods, HSV thresholding, the Segment Anything Model (SAM 2.1), and a CNN with a VGG16 encoder, were compared for performance. Among them, optimized HSV thresholding achieved the best balance between accuracy and simplicity, with a Dice score of 0.97 and a mean error of 8% on reference tests. The proposed setup provides a practical and reproducible method to study contact formation in soft robotics and a reproducible method for obtaining ground-truth data for tactile sensing and control.

Soft robots, compared to rigid robots, possess inherent advantages, including higher degrees of freedom, compliance, and enhanced safety, which have contributed to their increasing application across various fields. Among these benefits, adaptability is particularly noteworthy. In this article, adaptability in soft robots is categorized into external and internal adaptability. External adaptability refers to the robot’s ability to adjust, either passively or actively, to variations in environments, object properties, geometries, and task dynamics. Internal adaptability refers to the robot’s ability to cope with internal variations, such as manufacturing tolerances or material aging, and to generalize control strategies across different robots. As the field of soft robotics continues to evolve, the significance of adaptability has become increasingly pronounced. In this review article, we summarize various approaches to enhancing the adaptability of soft robots, including design, sensing, and control strategies. Additionally, we assess the impact of adaptability on applications such as surgery, wearable devices, locomotion, and manipulation. We also discuss the limitations of soft robotics adaptability and prospective directions for future research. By analyzing adaptability through the lenses of implementation, application, and challenges, this article aims to provide a comprehensive understanding of this essential characteristic in soft robotics and its implications for diverse applications.

Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. This neuron model can excite the natural dynamics of soft robotic snakes, and it enables distinct movements, such as turning or moving forward, by simply altering the neural thresholds. Finally, we demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. The high-level agent only needs to adjust the two thresholds to generate complex movement patterns, thus strongly simplifying the learning of reactive locomotion. Simulation results demonstrate that the proposed architecture significantly enhances the performance of the soft snake robot, enabling it to achieve target objectives with a 21.6% increase in success rate, a 29% reduction in time to reach the target, and smoother movements compared to the vanilla reinforcement learning controllers or Central Pattern Generator controller acting in torque space.

Motivated by the need for efficiency and robustness in repetitive robotic tasks such as locomotion, this study introduces the concept of Natural Motion Manifolds (NMMs) and presents a control method to stabilize and excite motions based on these structures. By considering the intersection of a Poincaré section with a surface comprising a continuum of autonomous evolutions, the proposed controller extends the linearized Poincaré map control from a single orbit to a family of orbits. This allows us to derive simple controllers to excite intrinsic nonlinear resonances and exploit the natural dynamics when varying the energy target (or the running velocity). We validated the method through simulations and experiments on a serial elastic quadruped. Relying on natural dynamics and minimal motor commands, we could implement a bounding gait at desired velocities without needing dynamic compensations. The experiments provide a thorough validation of the feasibility and the benefits of controlled, predictable, and purposeful oscillatory behavior via explicit excitation of a quadruped’s natural dynamics.

Dynamics-based control offers a promising approach to exploring the motion potential of soft robots. However, inherently infinite degrees of freedom of these systems pose significant challenges for dynamics modeling, closely followed by the pressing robustness concerns arising from finite-dimensional approximations. This paper addresses these issues by proposing a physics-informed dynamics learning neural network and an adaptive fractional-order control for continuum soft robots. Specifically, a deep Lagrangian neural network is first developed with an embedded self-attention mechanism to enhance learning efficiency, accuracy, and data sensitivity. Subsequently, an adaptive fractional-order sliding mode controller is designed, leveraging the inherent historical memory properties of fractional calculus. This controller not only ensures robust shape control but also improves response speed and tracking accuracy. To further handle model discrepancies in the learned dynamics and external disturbances, a nonlinear disturbance observer is introduced to effectively estimate and compensate for lumped uncertainties, thereby ensuring reliable performance. Theoretical analysis confirms the closed-loop stability, while both simulation and experiment results validate the high dynamics fitting accuracy of the proposed network, as well as the robust and precise tracking capability of the fractional-order controller.

While consciousness has been historically a heavily debated topic, awareness had less success in raising the interest of scholars. However, more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge “Awareness Inside” callwithin Horizon Europe, designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

Human fingers exhibit remarkable dexterity and adaptability through a combination of structures with varying stiffness levels, ranging from soft tissues (low stiffness) to tendons and cartilage (medium stiffness) to bones (high stiffness). This paper focuses on the development of a robotic finger that emulates these multi-stiffness characteristics. Specifically, we propose utilizing a lattice configuration, parameterized by voxel size and unit cell geometry, to achieve fine-tuned stiffness properties with high precision. A key advantage of this approach is its compatibility with single-process 3D printing, which eliminates the need for manual assembly of components with varying stiffness. Using this method, we present a novel, human-like robotic finger and a soft gripper. The gripper is integrated with a rigid manipulator and demonstrated in pick-and-place tasks, showcasing its effectiveness.

Soft robotics integrates engineering, materials science, and biology to tackle challenges that conventional robotics cannot solve. Alongside the advancements in soft robot technology, there is also a need for a standardized hardware platform that can enable benchmarking of various control methods developed for soft-bodied robots. This paper contributes to the state-of-the-art by designing a testbed that features a tendon-driven soft-bodied robot with integrated closed-loop force control.