Soft mechanical inductive sensors are emerging as a promising solution for advanced robotics, healthcare, and wearable devices, offering high precision, adaptability, and environmental robustness. These sensors leverage coil-based designs to achieve resilience against temperature variations, humidity, and mechanical wear, making them suitable for long-term operation in challenging environments. Existing reviews tend to focus on a single aspect of coil-based inductive sensors, thereby lacking a comprehensive, unified analysis of the field. This review addresses this gap by analyzing recent developments in soft mechanical inductive sensors, from theoretical concepts and practical design to their use cases. The review focuses on the working principles, design strategies, fabrication techniques, electronic interfaces, and, finally, the applications of coil-based soft mechanical inductive sensors. Key applications in soft robotics, prosthetics, and haptic devices are examined, highlighting the transformative potential of these sensors across diverse domains. Finally, the review discusses critical challenges, including sensitivity optimization, durability, and environmental interference, and outlines future directions to further advance this promising technology.

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

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.

Soft mechanical inductive (SMI) sensors are emerging as a promising solution for advanced robotics, healthcare, and wearable devices, offering high precision, adaptability, and environmental robustness. These sensors leverage coil-based designs to achieve resilience against temperature variations, humidity, and mechanical wear, making them suitable for long-term operation in challenging environments. Existing reviews tend to focus narrowly on specific applications of coil-based inductive sensors, such as soft-robotic tactile sensing or biomedical devices, without systematically comparing design methodologies, fabrication techniques, or broader use cases, thereby lacking a unified perspective on the field. This review addresses this gap by analyzing recent developments in SMI sensors from theoretical concepts and practical design to their use cases. The review focuses on the working principles, design strategies, fabrication techniques, electronic interfaces, and, finally, the applications of coil-based SMI sensors. Key applications in soft robotics, prosthetics, and haptic devices are examined, highlighting the transformative potential of these sensors across diverse domains. Finally, the review discusses critical challenges, including sensitivity optimization, durability, and environmental interference, and outlines future directions to further advance this promising technology.

In Distributed Manipulator Systems (DMS), decentralization is a highly desirable property as it promotes robustness and facilitates scalability by distributing computational burden and eliminating singular points of failure. However, current DMS typically utilize a centralized approach to sensing, such as single-camera computer vision systems. This centralization poses a risk to system reliability and offers a significant limiting factor to system size. In this work, we introduce a decentralized approach for sensing and in a Distributed Manipulator Systems using Neural Cellular Automata (NCA). Demonstrating a decentralized sensing in a hardware implementation, we present a novel inductive sensor board designed for distributed sensing and evaluate its ability to estimate global object properties, such as the geometric center, through local interactions and computations. Experiments demonstrate that NCA-based sensing networks accurately estimate object position at 0.24 times the inter sensor distance. They maintain resilience under sensor faults and noise, and scale seamlessly across varying network sizes. These findings underscore the potential of local, decentralized computations to enable scalable, fault-tolerant, and noise-resilient object property estimation in DMS