High-Bandwidth Biomimetic Finger for Tactile-Transparent Remote Texture Sensing

Shuang Yang; Fuyuan Liu; Yitian Shao

cs.ROAug 13, 2026

High-Bandwidth Biomimetic Finger for Tactile-Transparent Remote Texture Sensing

Authors: Shuang Yang, Fuyuan Liu, Yitian Shao

Organizations: School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China. · State Key Laboratory of Smart Farm Technologies and Systems, Harbin 150006, China.

Abstract

High-fidelity tactile feedback is essential for robotic teleoperation, enabling precise manipulation and critical decision-making. While biomimetic fingertip sensors can capture surface-texture features, how their design shapes the tactile transparency of rendered feedback remains poorly understood. This paper presents a biomimetic fingertip replicating the human finger's multilayer mechanical gradient and fingerprint morphology, with an embedded high-sensitivity inertial measurement unit capturing texture-induced vibrations for remote vibrotactile rendering. Three variants are compared with the human fingertip through temporal and spectral analyses and a user study spanning three perceptual dimensions, establishing a transmission chain from fingertip design through signal characteristics to tactile transparency. The multilayer mechanical gradient yields the clearest improvements in signal intensity and texture-feature representation, whereas a stiffer skin layer amplifies vibration intensity at the expense of feature representation. The perceptual dimensions draw on distinct signal attributes: roughness on energy scaling, granularity on spectral shape, and repetitivity on characteristic-peak representation. These findings offer design principles for application-specific fingertips in robotic teleoperation.

Figures & tables

Figure 1: A biomimetic fingertip capable of capturing micron-level textural details to deliver high-fidelity vibrotactile feedback. (a) Vibrotactile signals acquired by the biomimetic finger mounted on a Franka Research 3 robotic manipulator are remotely transmitted to the operator via a voice coil motor. (b) Structural comparison between the human fingertip anatomy and the proposed biomimetic design.

Figure 2: (a) Morphological comparison of three biomimetic fingertips with the human fingertip. Top: Optical macrographs showcasing finger profiles and epidermal ridge patterns; Bottom: Cross-sectional optical coherence tomography (OCT) images of the fingerprints along the sliding direction. (b) Surface characterization of eleven textured substrates. Top: Photographs displaying material surfaces and distinct textures; Bottom: Cross-sectional OCT profiles of the textures captured along the red line indicating the sliding path.

Figure 3: Schematic illustration of the fabrication process for biomimetic fingers with distinct designs. I)/II) Modeling of key components to integrate the 3D-printed bone and IMU unit, using PDMS or Ecoflex-30 as the soft filling material. III) Fabrication of PDMS skin via molding with custom 3D-printed molds. IV) Fabrication of TPU-95A skin via direct 3D printing.

Figure 4: Experimental setup and signal comparison overview. (a) The Franka Research 3 robotic arm with a biomimetic fingertip mounted on the customized end-effector; an IMU embedded within the biomimetic fingertip and a second IMU affixed to the human subject’s index finger recorded vibrotactile signals during sliding. (b) Maximum cross-correlation coefficients between the vibrotactile signals of each biomimetic fingertip and the human fingertip, computed across 11 textured surfaces at three sliding speeds (10, 20, and 50 mm/s); the grey dashed lines indicate the mean correlation coefficient across all 11 surfaces. (c) Representative temporal waveforms at 20 mm/s for the three biomimetic fingertips and the human fingertip.

Fingertip	10 mm/s	20 mm/s	50 mm/s
PDMS	0.193	0.189	0.281
PDMS+Ecoflex	0.210	0.149	0.193
TPU+Ecoflex	0.137	0.125	0.200

Table 1: Mean maximum cross-correlation coefficients between the vibrotactile signals of each biomimetic fingertip and the human fingertip at three sliding speeds. Each value averages 99 coefficients (11 surfaces

\times

\times

3 repetition pairs), corresponding to the grey dashed lines in Figure 4(b).

Figure 5: Spectral analysis of vibrotactile signals captured by the three biomimetic fingertips and the human fingertip across 11 textured surfaces (M1-11, one per row), indicated by line colors. The semi-transparent and solid lines represent the FFT spectrum and the Welch power spectral density displayed on a logarithmic axis, respectively. Three blocks of columns correspond to sliding speeds of 10, 20, and 50 mm/s. The dashed lines in subplots of periodic surfaces (M1-3) mark the characteristic frequency and its first harmonic, determined by the texture spatial period and the sliding speed.

	Peak Prominence (-)						150-300 Hz band energy ( $\times 10^{3}$ (mm/s 2 ) 2 )
	M1: 0205 Sine		M2: 0210 Sine		M3: 0220 Sine		M4: 005 Random	M5: 010 Random	M6: 020 Random
Fingertip	$f_{0}$	$2f_{0}$	$f_{0}$	$2f_{0}$	$f_{0}$	$2f_{0}$
PDMS	1.98	2.76	2.46	2.53	4.21	1.66	5.37	6.24	4.08
TPU+Ecoflex	5.05	2.80	2.50	6.00	3.10	3.44	484	436	474
PDMS+Ecoflex	4.89	3.63	3.86	3.51	3.04	2.60	45.9	74.9	133
Human	3.36	3.31	3.24	3.43	3.85	1.65	0.420	0.430	0.396

Table 2: Spectral feature metrics of the three biomimetic fingertips and the human fingertip at the sliding speed of 20 mm/s.

Figure 6: Spectral cosine similarity between each biomimetic fingertip and the human fingertip, computed on min-max normalized FFT spectra within two frequency bands (0-150 Hz and 150-300 Hz) across 11 textured surfaces at three sliding speeds (10, 20, and 50 mm/s); the grey dashed lines indicate the mean similarity across all 11 surfaces under each (finger, speed, band) condition.

Figure 7: User study scenarios and perceptual evaluation. (a) Experimental setups for Test 1 (bare-hand exploration) and Test 2 (motor-delivered vibrotactile feedback). (b) Rating distributions from 30 participants across three biomimetic fingertips, 11 surfaces, and three perceptual dimensions (Roughness, Repetitive, Granularity). Scatter points indicate $p$ -values from Wilcoxon signed-rank tests evaluating consistency with bare-hand ratings.

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Robot Teleoperation Haptic Feedback Contact-Rich Robotic Manipulation

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Enabling operators to perceive and interact with remote environments naturally is a fundamental challenge in robotic teleoperation. This is especially critical in tasks involving physical interaction, where real-time haptic awareness improves operational safety and effectiveness. Existing kinesthetic haptic feedback methods suffer from instability during rigid surface contacts and remain sensitive to communication delays, while visual cue-based force feedback imposes additional cognitive load and limits sustained situational awareness. This work presents a teleoperation interface that conveys remote surface interactions to the operator through electrovibration-based tactile feedback, enabling naturally mapped force reflection while avoiding the stability issues associated with kinesthetic feedback and the latency limitations of mechanical actuators. A user study (N=21) evaluated interface usability, sense of presence, and operator workload under two force reflection conditions: visual feedback and electrovibration-based tactile feedback. Characterisation experiments further assessed path-following accuracy and response time across both conditions. Results show that tactile feedback significantly reduced response time by 15.35% (p=0.002, d=0.96) and increased the sense of presence by 31% (p<0.001, d=0.90) compared to visual feedback, while imposing comparable workload and usability across both conditions. These findings demonstrate that electrovibration-based tactile feedback is a viable and effective modality for robot teleoperation, improving operator responsiveness and sense of presence in contact-rich manipulation tasks, with direct applicability to safety-critical domains such as nuclear maintenance.

Abstract

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