External Photoreflective Tactile Sensing Based on Surface Deformation Measurement

Seiichi Yamamoto; Hiroki Ishizuka; Takumi Kawasetsu; Koh Hosoda; Takayuki Kameoka; Kango Yanagida; Takato Horii; Sei Ikeda

cs.RONov 9, 2025

External Photoreflective Tactile Sensing Based on Surface Deformation Measurement

Authors: Seiichi Yamamoto, Hiroki Ishizuka, Takumi Kawasetsu, Koh Hosoda, Takayuki Kameoka, Kango Yanagida, Takato Horii, Sei Ikeda, +1 more

Organizations: Graduate School of Engineering Science, The University of Osaka, Toyonaka 560-0043, Japan

Abstract

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.

Figures & tables

Fig. 1: Layout example of the proposed tactile-sensing method. When a soft robot performs a tactile interaction with an object, a deformation distribution arises on its skin surface. By measuring this deformation using a tactile-sensing module attached externally to the skin, force information can be obtained without placing a transducer directly at the contact interface.

Fig. 2: Principle of the proposed tactile-sensing method. (a) A tactile-sensing module containing a photoreflector, which emits light and measures the intensity of the reflected light, is attached to a soft body such as silicone rubber. (b, c) When an external force deforms the soft body, the distance between the sensing element and the reflective surface changes. This change in distance causes a change in the amount of reflected light. (d) The variation in the amount of the reflected light causes the variation in output voltage of the photoreflector. The applied external force can then be estimated from the variation in output voltage corresponding to the variation in received light intensity.

Fig. 3: The output voltage of the photoreflector was evaluated as a function of the color of the silicone rubber and the intensity of the reflected light. (a) Experimental setup: A linear stage was used to vary the distance between the colored silicone rubber and the photoreflector, and the resulting changes in output voltage were measured. (b) The output voltage increased with increasing distance. The output characteristics also varied depending on the color of the silicone rubber. According to the experimental result, a mixture ratio of 75% white and 25% black silicone rubber was adopted in this study.

Fig. 4: Prototype tactile sensor and tactile-sensing module developed in this study. (a) Photograph of the actual sensor and its dimensions. (b) Illustration of the tactile-sensing module and its dimensions. The module is simply fixed to the silicone rubber, representing a straightforward implementation of tactile information sensing.

Fig. 5: A finite-element model was used for the analysis. (a) The model consists of a structure representing the silicone rubber and the transducer. (b) Before indentation is applied. (c) After indentation is applied. Applying the indentation causes deformation on the side surface of the silicone rubber. This deformation value is used to calculate the theoretical change in output voltage.

Symbol	Definition	Values
$C_{01}$	Material constant	$1.218\times 10^{-2}\$ \mathrm{M}\mathrm{P}\mathrm{a}$$
$C_{10}$	Material constant	$-3.335\times 10^{-5}\$ \mathrm{M}\mathrm{P}\mathrm{a}$$

TABLE I: Parameters of silicone rubber [ 37 ] .

Symbol	Definition	Values
$E$	Young’s modulus	$1.628\times 10^{9}~$ \mathrm{P}\mathrm{a}$$
$\nu$	Poisson’s ratio	$4.089\times 10^{-1}$
$K$	Bulk modulus	$2.978\times 10^{9}~$ \mathrm{P}\mathrm{a}$$
$G$	Shear modulus	$5.778\times 10^{8}~$ \mathrm{P}\mathrm{a}$$
$\rho$	Density	$1.030\times 10^{3}~$ \mathrm{k}\mathrm{g}\mathrm{/}\mathrm{m}^{3}$$
$\sigma$	Tensile yield strength	$2.744\times 10^{7}~$ \mathrm{P}\mathrm{a}$$

TABLE II: Parameters of ABS resin at

23\text{\,}\mathrm{\SIUnitSymbolCelsius}

Fig. 6: Configuration of the experimental setup. The setup consisted of a positioning table robot, a force gauge, a data logger, and a laptop PC. Force was applied to the sensor using the positioning table robot, while the outputs from both the force gauge and the sensor were recorded by the data logger and monitored on the PC.

Fig. 7: Evaluation results of the tactile-sensing module’s response to applied force. The vertical axis represents the change in the module output (inverted photoreflector output, see Section III-A). (a) Relationship between the applied force and indentation of the silicone rubber. A monotonic increasing relationship was observed between force and indentation. (b) Relationship between the indentation and the output of the tactile-sensing module, along with the theoretical values. A monotonic increasing relationship was observed between indentation and output. The close agreement between the theoretical and measured values indicates that the tactile sensor was well modeled. (c) Relationship between the applied force and the output of the tactile-sensing module. Owing to the low viscoelasticity of the silicone rubber, favorable hysteresis characteristics were obtained.

Fig. 8: Sensor response under repeated loading. The vertical axis represents the change in the module output (inverted photoreflector output, see Section III-A). (a) The output of the tactile-sensing module remained constant regardless of the number of loading cycles. (b, c) Outputs at the beginning and end of the experiment. In both cases, the voltage changes closely followed the changes in the applied force, regardless of the number of cycles, demonstrating high repeatability.

Fig. 9: Repeatability analysis results. (a) Relationship between the voltage output and the applied load obtained from 99 segmented loading trials. The mean and standard deviation are plotted, and the standard deviation is very small, indicating good repeatability. (b) Time histories of the mean voltage and force over the 98 segmented loading-unloading cycles. The standard deviation is also very small, confirming good repeatability.

Fig. 10: Sensor response under different loading and unloading speeds: (a) 0.1 mm/s, (b) 1 mm/s, and (c) 10 mm/s. The vertical axis represents the change in the module output (inverted photoreflector output, see Section III-A). In all loading conditions, the output closely followed the applied force, indicating that the output followed the applied force, with speed-dependent lag. This behavior is attributed to the elasticity and low viscoelasticity of the silicone rubber.

Fig. 11: Results for hysteresis: (a) 0.1 mm/s, (b) 1 mm/s, and (c) 10 mm/s. The hysteresis was derived from the results of five repeated trials. The maximum hysteresis error was approximately 6–8%FS.

Fig. 12: Relationship between the applied force and the sensor output under different temperature conditions. (a) 21.1 °C, (b) 27.2 °C, and (c) 31.2 °C. No variation in sensor output was observed under these temperature conditions, confirming that the sensor output remains stable in a room-temperature environment.

Fig. 13: Experimental setup using a gripper and the corresponding results. (a) Dimensions of the fabricated soft finger. A resin frame was embedded inside, and the structure was covered with silicone rubber. Attaching the tactile-sensing module to the gripper created a soft robotic finger. (b) Overview of the experimental setup. The gripper was used to grasp a plastic rod. Signals from the tactile-sensing module were recorded on a PC, which also output control signals to the gripper. (c) Results of the grasping experiment. The object was grasped, and the output of the tactile-sensing module changed accordingly.

Work	Principle	Force Range	Sensitivity	Response	Hysteresis (as reported)	Repeatability
This Work	External Photoreflector + Skin Deformation	$\sim$ 7 N@3 mm	0.07 V/N	1 kHz; lag 5–706 ms	6.50–7.88%FS (Mean $\pm$ SD, n=5)	NR
[ 35 ]	Optical Taxel Array (Embedded Pad)	0–15 N	0.018 V/N	500 Hz; 9.2 ms	5-6% (Error)	1.32% (Error)
[ 28 ]	Capacitive (Sealed Liquid)	0–1 N	0.169 pF/N	20 ms	6.28%	NR
[ 18 ]	Optical Microfiber	0–10 N	5.4%/N (0–1 N)	10 ms integration	NR	NR
[ 16 ]	3-Axis Capacitive	0–190 kPa ( $\sim$ 12 N)	14.22 /N	Less than 40 ms	NR	NR
[ 46 ]	Piezoresistive Elastomer	NR	NR	NR	6.8% (Area-Based)	Drift $\leq$ 4%

TABLE III: Performance comparison with representative tactile sensors. Values are reported as stated in each reference; NR indicates not reported. Definitions and test conditions vary across studies.

Work	Off-Interface	Embed in Skin	Replaceable	Wiring	Spatial Capability
This Work	Yes	No	Yes	Low	Single-Point (Scalable)
[ 35 ]	No	Yes	No	High (PCB)	Array
[ 28 ]	Yes	Yes	Partial	Low	Single-Point
[ 18 ]	Yes	Yes	Partial	Low	Single-Point
[ 16 ]	No	Yes	No	High	3-axis point
[ 46 ]	No	No	No	Med	Array

TABLE IV: Integration-oriented comparison for soft-robot deployment.

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FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems

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Columbia University

Tactile Robotic Systems Cross-Embodiment Transfer Open-Source

Open paper AskPDF arXiv

We present FlexiTac, a low-cost, open-source, and scalable piezoresistive tactile sensing solution designed for robotic end-effectors. FlexiTac is a practical "plug-in" module consisting of (i) thin, flexible tactile sensor pads that provide dense tactile signals and (ii) a compact multi-channel readout board that streams synchronized measurements for real-time control and large-scale data collection. FlexiTac pads adopt a sealed three-layer laminate stack (FPC-Velostat-FPC) with electrode patterns directly integrated into flexible printed circuits, substantially improving fabrication throughput and repeatability while maintaining mechanical compliance for deployment on both rigid and soft grippers. The readout electronics use widely available, low-cost components and stream tactile signals to a host computer at 100 Hz via serial communication. Across multiple configurations, including fingertip pads and larger tactile mats, FlexiTac can be mounted on diverse platforms without major mechanical redesign. We further show that FlexiTac supports modern tactile learning pipelines, including 3D visuo-tactile fusion for contact-aware decision making, cross-embodiment skill transfer, and real-to-sim-to-real fine-tuning with GPU-parallel tactile simulation. Our project page is available at https://flexitac.github.io/.

Abstract

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