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.

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

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.

Monolithic Units: Actuation, Sensing, and Simulation for Integrated Soft Robot Design

This work introduces the Monolithic Unit (MU), an actuator-lattice-sensor building block for soft robotics. The MU integrates pneumatic actuation, a compliant lattice envelope, and candidate sites for optical waveguide sensing into a single printed body. In order to study reproducibility and scalability, a parametric design framework establishes deterministic rules linking actuator chamber dimensions to lattice unit cell size. Experimental homogenization of lattice specimens provides effective material properties for finite element simulation. Within this simulation environment, sensor placement is treated as a discrete optimization problem, where a finite set of candidate waveguide paths derived from lattice nodes is evaluated by introducing local stiffening, and the configuration minimizing deviation from baseline mechanical response is selected. Optimized models are fabricated and experimentally characterized, validating the preservation of mechanical performance while enabling embedded sensing. The workflow is further extended to scaled units and a two-finger gripper, demonstrating generality of the MU concept. This approach advances monolithic soft robotic design by combining reproducible co-design rules with simulation-informed sensor integration.

Neural Cellular Automata for Decentralized Sensing using a Soft Inductive Sensor Array for Distributed Manipulator Systems

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