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.

The development of advanced control strategies for prosthetic hands is essential for improving performance and user experience. Soft prosthetic wrists pose substantial control challenges due to their compliant structures and nonlinear dynamics. This work presents a learning-based impedance control strategy for a tendon-driven soft continuum wrist, integrated with the PRISMA HAND II prosthesis, aimed at achieving stable and adaptive joint-space control. The proposed method combines physics-based modeling using Euler-Bernoulli beam theory and the Euler-Lagrange approach with a neural network trained to estimate unmodeled nonlinearities. Simulations achieved a Root Mean Square Error (RMSE) of (Formula presented.) rad and a settling time of 3.1 s under nominal conditions. Experimental trials recorded an average RMSE of (Formula presented.) rad and confirmed the controller’s ability to recover target trajectories under unknown external forces. The method supports compliant interaction, robust motion tracking, and trajectory recovery, positioning it as a viable solution for personalized prosthetic rehabilitation. Compared to traditional controllers like Sliding Mode Controller (SMC), Model Reference Adaptive Controller (MRAC), and Model Predictive Controller (MPC), the proposed method achieved superior accuracy and stability. This hybrid approach successfully balances analytical precision with data-driven adaptability, offering a promising pathway towards intelligent control in next-generation soft prosthetic 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.

As global demand for fruits and vegetables continues to rise, the agricultural industry faces significant challenges in securing adequate labor. Robotic harvesting devices offer a promising solution to address this issue. Harvesting delicate fruits, such as blackberries, presents unique open challenges due to their fragility. This paper introduces BerryTwist, a prototype robotic gripper specifically designed for blackberry harvesting. The gripper features a fabric tube mechanism that uses motorized twisting action to gently envelop the fruit, ensuring uniform pressure application and minimizing damage. The twisting motion is transferred to the tube through a compliant mechanism, thus maintaining the overall softness of the structure. We thoroughly tested BerryTwist, paying particular attention to the effect of varying tube properties. We developed three types of tubes varying in elasticity and compressibility, using foam padding, spandex, and food-safe cotton cheesecloth. Performance testing focused on assessing each gripper's ability to detach and release blackberries, with an emphasis on quantifying damage rates. The results indicate that the proposed gripper achieved an 82% success rate in detaching blackberries and a 95% success rate in releasing them, demonstrating its promising potential for robotic harvesting applications. Finally, we will demonstrate the robotic harvesting operation by establishing a simple farm setup and integrating the gripper with Franka Emika's robot manipulator.