Surface-based Manipulation Using Tunable Compliant Porous-Elastic Soft Sensing
Authors: Gayatri Indukumar, Muhammad Awais, Diana Cafiso, Matteo Lo Preti, Lucia Beccai
Organizations: Soft BioRobotics Perception Group at Istituto Italiano di Tecnologia, 16163, Genova, Italy · University of Genoa, 16126, Genova, Italy · Biorobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy · Soft Robotics Lab, National University of Singapore, 117575, Singapore
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.
Figures & tables
Figure 1: Overview of the proposed COPESS-based surface manipulation system. (a) Photograph of the COPESS-based surface manipulation setup, consisting of a sensing unit with a 4×4 coil array, a lattice-based modulating layer, and a target layer. (b) Schematic illustration of the COPESS-based manipulation mechanism. The sensorized tile is covered with a 3D-printed lattice structure, which acts as a modulating layer. As the object transitions from a high-stiffness lattice to a low-stiffness lattice region, its velocity decreases while the sensor response increases.
Figure 2: Mechanical and electrical characterization informing the sensor’s design. (a) Force vs. displacement curves of three different lattice structures, demonstrating that the Gyroid (blue) offers the best combination of initial stiffness and strain range before densification. (b) Comparing a gyroid lattice structure printed using Elastic 50AV2 resin against the characteristic curve of a typical hyperelastic material. (c) Inductance change as a function of target distance, which heuristically determined the chosen thickness for the modulating lattice layer.
Figure 3: Quantitative results demonstrating the co-design of mechanical and sensing properties by tuning lattice relative density. Inductance-force curves for (a) 7% , (b) 10% , and (c) 20% relative densities, illustrating how a higher relative density increases the operational force range at the cost of sensitivity (the slope of the curve). The data are presented as mean and confidence interval.
Figure 4: Experimental validation of the sensor’s robustness. (a) EM crosstalk when a linear vertical force is applied on S1 and the corresponding sensor response from its three neighbouring sensors. (b) Results from a 200-cycle mechanical repeatability test. The 99.96% overlap between the first three and last three cycles (inset) demonstrates the high durability and minimal plastic deformation of the 3D-printed gyroid structure.
Figure 5: Localized sensing and passive guidance of a manipulated object. The object is moved across the sensorized surface as it transitions from a low-stiffness region to a high-stiffness region, with its position tracked in a LabVIEW GUI. Two types of objects are demonstrated: Top Row: Rigid standard weight, Bottom Row: Irregular soft object
Figure 6: Object velocity and travel distance on tiles with varying relative densities. (a) The 20% region, being stiffer, exhibits lower friction, allowing continuous motion, whereas lower relative densities increase friction and cause faster deceleration. (b) Passive guidance of the object across regions with different relative densities. In the 20% region, the object maintains motion with minor velocity drops, while in the 7% region, it comes to rest rapidly due to higher friction.
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.
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
This paper introduces a novel tactile sensor for in-hand manipulation with slip-aware control that integrates velocity and force/torque sensing with pressure map estimation into a single device with a deformable contact pad. To the best of our knowledge, this is the first sensor to combine these sensing modalities within a single compliant structure. The sensor features a deformable contact surface and can robustly track both flat and curved surfaces across a wide range of diffuse surface materials. Its performance is evaluated through a comprehensive set of experiments that highlight both its capabilities and limitations. The sensor is designed for rapid and low-cost fabrication using a combination of standard PCB manufacturing and rapid prototyping techniques.
Gabriel Arslan Waltersson, Yiannis Karayiannidis
Department of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden · Department of Automatic Control, Lund University, Sweden