Flexible Piezoelectric Tactile Sensing System for Intelligent Object Recognition

Razan Khalifeh; Mohamad Yaacoub; Christian Gianoglio; Moustafa Saleh; Maurizio Valle; Yahya Abbass

cs.RONov 5, 2026

Flexible Piezoelectric Tactile Sensing System for Intelligent Object Recognition

Authors: Razan Khalifeh, Mohamad Yaacoub, Christian Gianoglio, Moustafa Saleh, Maurizio Valle, Yahya Abbass

Organizations: Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy · Department of Computer and Communication Engineering, Lebanese International University (LIU), Beirut, Lebanon

Abstract

Tactile sensing provides information about object properties that can improve robotic grasping and manipulation. However, accurate object recognition during the early stages of grasping remains challenging, particularly under strict real-time constraints. This study presents a flexible, distributed piezoelectric tactile sensing system for early-stage object recognition. The system was experimentally evaluated using six everyday objects and three recognition approaches: feature-based classification using support vector machines (SVMs), raw-signal classification using 1D convolutional neural networks (1D-CNNs) and convolutional recurrent neural networks (C-RNNs), and spatiotemporal heatmap classification using a 2D-CNN. The effects of sensor count and temporal window size on recognition accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50 samples), was deployed on an embedded edge platform, achieving an end-to-end recognition latency of approximately 101 ms from contact onset and an energy consumption of 63.8 microjoules per inference. These results demonstrate the potential of distributed piezoelectric tactile sensing for accurate, energy-efficient, and real-time object recognition during robotic grasping.

Figures & tables

References	Sensor type	Mechanical structure	Number of sensors	Spatial resolution	Application	Hardware deployment	Response time
[ 4 ]	Piezoresistive	Flexible	64	2.5 mm	Object recognition	✗	✗
[ 5 ]	Piezoresistive	–	1	27.6 taxels/cm 2	Material classification	✗	103 ms (GPU)
[ 6 ]	Piezoresistive	Rigid	256	3.5 mm	Object recognition	✗	✗
[ 7 ]	Capacitive	Flexible	–	–	Object recognition	✗	✗
[ 8 ]	Capacitive	Flexible	64	2 mm	Material recognition	✗	✗
[ 9 ]	Piezoelectric	Flexible	1	–	Object surface recognition	✗	✗

TABLE I: Overview of most relevant existing work on tactile-based object recognition.

Fig. 1: (a) Top: Single sensor structure and Bottom: layout of the sensing array. (b) Development procedure of the sensing caps (c), System block diagram (d), Sensing system mounted on the Tiago gripper.

Sensitivity	Linearity	SINAD
1.5–31.96 pC/N	2.3% nonlinearity error	56 dB

TABLE II: Sensor characterization metrics for the proposed sensing system.

Fig. 2: Channel-to-channel consistency of three sensors. The charge response is shown as a function of the applied force for sensors S5, S11, and S1.

Fig. 3: (a) Response of a single sensor to a sine stimulus at variable frequencies (1 Hz - 100 Hz). (b) Response of a single sensor to a square stimulus at variable forces (0.12 N - 6 N).

Fig. 4: (a) Set of daily-use objects, (b) TIAGo robot manipulating a PB, and (c) the corresponding sensor response.

Panel of Figure 7 — Algorithm 1 Energy Computation

Fig. 6: Block diagram of object recognition approaches: End-to-end (EEA), Feature-Based Approach (FBA), and Heatmap Approach (HMA).

	Sensor Group
Approach	$N_{c}=16$	$N_{c}=24$	$N_{c}=32$
EEA	$\mathcal{D}^{W}_{g3}$	$\mathcal{D}^{W}_{g4}$	$\mathcal{D}^{W}$
FBA	$\tilde{\mathcal{D}}^{W}_{g3}$	$\tilde{\mathcal{D}}^{W}_{g4}$	$\tilde{\mathcal{D}}^{W}$
HMA	$\mathcal{H}^{W}_{g3}$	$\mathcal{H}^{W}_{g4}$	$\mathcal{H}^{W}$

TABLE III: Summary of the datasets

Panel of Figure 10 — Algorithm 2 HeatMap Construction

Fig. 7: Illustration of the heatmap constructed from the tactile signals with the Dataset $\mathcal{H}^{50}$ .

Algorithm	Hyperparameter	Values
CNN	$F$ (Filters)	$\{8,16,32,64\}$
CNN	$K$ (Kernel Size)	$\{2,3,4\}$
SVM	$C$ (Penalty)	$\{0.01,0.1,1,10,100\}$
SVM	$\gamma$ (Kernel Coefficient)	$\{0.01,0.1,1,10,100\}$
C-RNN	$F$ (Filters)	$\{8,16,32,64\}$
C-RNN	$K$ (Kernel Size)	$\{2,3,4\}$

TABLE IV: Summary of Hyperparameter Configurations for Each Algorithm

Fig. 8: Radar plots presenting the performance metrics of the implemented algorithms across different window size ( $W$ ) and sensor groups ( $N_{C}$ ).

Approach	Dataset	F1-score
EEA	$\mathcal{D}^{50}_{g3}$	0.8824
	$\mathcal{D}^{50}_{g4}$	0.9102
	$\mathcal{D}^{50}$	0.9532
FBA	$\tilde{\mathcal{D}}^{50}_{g3}$	0.9445
	$\tilde{\mathcal{D}}^{50}_{g4}$	0.9604
	$\tilde{\mathcal{D}}^{50}$	0.9661

TABLE V: Table presenting the highest F1-score values achieved with

W

=50, by the three approaches including C-RNN, SVM, and 2D-CNN across the three datasets.

Fig. 9: Performance of the implemented models in each of the three approaches for the three sensor groups using the smallest window size ( $W=50$ ).

Dataset	$\mathcal{H}^{50}$
Inference time (ms)	0.8817
Flash Size (kB)	7.65
kFLOPs	11.3
Power (mW)	72.3
Energy (µJ)	63.8

TABLE VI: Performance Metrics of the Best Performing Model (2D-CNN)

Figure 18

Tactile Image Recognition

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Apr 30, 2026J/K move · Enter open · S save

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

Binghao Huang, Yunzhu Li

Columbia University

Tactile Sensing Tactile Sensing for Robotic Manipulation Robotic Tactile Sensing

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

Tactile Image Recognition

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