Tactile sensing provides robots with contact information difficult to obtain from vision alone. Shear force is particularly important for understanding tangential contact, guiding corrective motions, and maintaining grasp stability, yet flexible and low-cost tactile arrays often measure only normal force. We introduce VecTile, a low-cost, 6 mm thin, flexible tactile array capable of three-axis force sensing. VecTile combines a piezoresistive layer with passive silicone nubs that convert normal and shear forces into distinguishable pressure patterns. The sensor can be fabricated in-house without specialized equipment. The complete system costs less than 35 USD for a 4x4 array. We validate the sensing mechanism through finite element analysis and hardware experiments. In controlled single-taxel tests over 1-5 N normal force and up to 1.5 N shear force, VecTile achieves root-mean-square errors of 0.39 N and 0.16 N for normal and shear forces. A VecTile calibrated while flat retains accuracy when curved, while a five-minute manual calibration is sufficient for practical use. Two applications demonstrate its utility. First, shear readings are sensitive enough to stop a sharp scalpel from puncturing a balloon after contact. Second, the sensor substantially improves Diffusion Policy-guided power-plug insertion. All designs and code will be released upon paper acceptance. Project website: https://jiannnh.github.io/VecTile/.
Figures & tables
Fig. 1: VecTile on Fin-Ray type fingers during plug insertion. Color denotes normal response and arrows indicate shear magnitude and direction.
Fig. 2: VecTile design. (A) The FPC implements a 4×4 taxel array, with four quadrants beneath each nub-based taxel, addressed as an 8×8 row–column matrix. During scanning, one column is driven at a time, while one row is connected to the virtual-ground front end. Compression reduces the resistance of the silver-fiber resistive layer, increasing current between paired electrodes within a quadrant. (B) The assembled sensor stacks the cast silicone and resistive layer over the FPC. Normal force raises the four quadrant readings uniformly, whereas shear creates across-quadrant difference.
Fig. 3: Finite element analysis of the nub-lever mechanism. (A) Force encoding and mechanical separability for a 3.0mm nub. (i) Under a 2N normal preload, the +x and −x half-plane forces are equal. Applying 1N of shear shifts the contact pressure toward the leading half, producing a differential D . (ii) Simulated loads mapped into the (Σ,D) reading space. Normal-force increments (blue) move mainly along Σ , whereas shear-force increments (red) move mainly along D . Each (Σ,D) reading maps to a unique (Fz,Fx) pair over the simulated positive- x range. (B) Effect of nub height on the mechanical response. (i) Normal force against indentation depth. (ii) Total contact force Σ within the nub footprint against applied normal force Fz . (iii) Differential D against applied shear shows the increase in shear gain with nub height; the lines show the 2N preload, and the shaded bands show D ’s span across the 1 – 5N preload range.
Fig. 4: VecTile fabrication. (1) Mold release is applied to the 3D-printed PLA mold components. (2) Dragon Skin 30 is mixed and vacuum-degassed. (3) The bottom plate is filled with the degassed silicone, after which the dummy FPC and spacer are positioned and a second pour completes the mold stack. (4) The mold is closed, clamped, and cured for 16h ; the silicone layer is then de-molded and the dummy FPC is slid out. (5) Cyanoacrylate glue dots are applied only at the marked locations on the FPC. (6) The resistive layer is placed over the FPC. (7) The FPC and resistive layer are slid into the molded silicone layer produced in step (4). (8) A small batch of Dragon Skin 30, prepared as in step (2), is injected through the openings; the bottom plate from step (1) is reused as a press under a 200g load during the approximately 8h cure.
Item
Cost (USD)
Sensor
FPC (30-pc order)
0.89
Resistive layer ( ≈ 0.004 m 2 at $30/m 2 )
0.12
Dragon Skin 30 ( ≈ 15 g at $20/lb trial kit)
0.70
FFC cable + connector set
1.50
Mold filament ( ≈ 30 g at $12/kg)
0.36
Subtotal
3.57
TABLE I: Bill of Materials
Fig. 5: Left: VecTile without the silicone layer mounted on an ATI Mini40 force/torque sensor, with a guide plate aligning the loading pin to each taxel. Right: Per-quadrant ADC response to normal force; the shaded envelope spans 64 quadrants. The quadratic is fitted over Fz≤1.25N .
Fig. 6: Sensor characterization. (A) Experimental setup for flat and R=85mm curved mountings. (B) Data collection procedure.
Fig. 7: Measured response and manual force decoding. (A) Effect of nub height on (i) stiffness, (ii) the normal-force feature Σ , and (iii) the shear-force feature D . Solid lines are the median while the bands show the range, both across 16 taxels. (B) Manual bench validation of a 3.0mm VecTile mounted on an ATI Mini40: hand loading (left) and a real-time comparison between the VecTile estimates and F/T reference measurements (right).
RMSE (N)
Nub height (mm)
Curved, 3.0 mm
0.6
1.0
2.0
3.0
convex
concave
Fz
0.45 ± 0.06
0.45 ± 0.05
0.41 ± 0.04
0.39 ± 0.03
0.45 ± 0.06
0.42 ± 0.04
Shear
0.29 ± 0.04
0.28 ± 0.04
0.24 ± 0.03
0.16 ± 0.02
0.21 ± 0.04
0.23 ± 0.03
TABLE II: Force RMSE across nub heights and curved mounts.
Fig. 8: VecTile demonstrations. (A) Scalpel–balloon reflex: (i) shear measured during a burst trial and (ii) shear-triggered retraction before puncture. (B) Visuotactile plug insertion: (i) Hardware setup and (ii) Camera views and fingertip tactile readings during a 3-DoF tactile sensing plug-insertion rollout.
Observation
Successful trials
Direct inserts
Successful corrections
Vision only
12/25 (48%)
5
7/20 (35%)
+ normal tactile
16/25 (64%)
2
14/23 (61%)
+ 3-DoF tactile
22/25 (88%)
4
18/21 (86%)
TABLE III: Plug-insertion performance at unseen locations.
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/.
This paper presents a scalable, open-source visuotactile sensing system for tensegrity robots that enables six-axis wrench estimation and contact detection. The proposed endcap sensor integrates an elastomeric shell, a 3D-printed thermoplastic polyurethane (TPU) interface, and a rigid base housing an embedded camera and LED illumination ring. A novel gyroid-infill bonding technique is introduced to form a durable elastomer-TPU interface without adhesives, yielding a lightweight and modular design compatible with large-scale tensegrity structures. A tactile-to-wrench neural network maps shear vector fields to six-dimensional force and torque measurements. Experimental results demonstrate accurate and stable wrench estimation with a mean squared error (MSE) of 0.1531 on static validation data and out-of-domain generalization under dynamic motion. Furthermore, full-system integration on a 12 kg tensegrity robot confirms the sensor's ability to reliably identify ground contacts. The system substantially improves the practicality of tactile feedback for tensegrity robots, offering a low-cost, reproducible, and physically interpretable pathway toward contact-aware proprioception and state estimation. Open source files are available at github.com/Jonathan-Twz/tensegrity-gelfoot
Wenzhe Tong, Jonathan Mi, Xili Yi +2
Robotics Department, University of Michigan, Ann Arbor, MI, 48109, USA
Robotic minimally invasive surgery has revolutionized surgical practice. However, as the surgeons are mechanically separated from the surgical tools, loss of tactile feedback occurs. We present a compact force sensor with three, millimeter scale sensing elements (taxels) that can be affixed to the grasping face of a robotic surgical tool. The sensor features a miniaturized design using Hall effect sensors and magnets embedded in a deformable elastomer contact layer. Sensor calibration is achieved with a 6 degree of freedom (DoF) parallel robot. Each taxel can detect applied normal and shear forces with average RMSE of 2.133 kPa. The calibrated sensor was validated on phantom and ex vivo porcine tissue, demonstrating tactile sensing during retraction as well as slip. This work introduces a small-scale Hall effect based tactile sensor, enabling sensing for force-sensitive robotic surgical applications.
Charles DeLorey, Michael Brockdorff, Adam Metcalf +3
STORM Lab, School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK