We present OpenTac, a novel sensor and a full-stack ecosystem for tactile skins that are highly customizable, scalable, and readily deployed in downstream robot control and learning applications. Our core contribution is twofold. First, we develop a scalable resistive tactile skin with a structured deformable overlay that converts tangential loading into spatially varying pressure. Differential responses across neighboring "taxels" support decoding of local shear vectors, and the common-mode response encodes normal force. Second, we provide a highly customizable sensor design tool that is able to automatically generate fabrication files for these tactile skins from 2D outlines and developable 3D surfaces, including from abstract descriptions such as text (e.g., "design a sensor for a size 9 shoe insole"). We develop an ecosystem around the OpenTac sensor, including the CAD design tool, a sensor simulator, cross-sensor transfer, and fast/adaptive data acquisition. We demonstrate the broad applicability of the OpenTac-CAD sensor design tool by designing and fabricating more than 30 sensors across a variety of robot embodiments. We characterize the three-dimensional tactile fields produced by OpenTac with a commercial, high-precision force/torque sensor. We demonstrate the broad applicability of 3D tactile sensing across contact-rich feedback control and manipulation policy learning on four real-world robot embodiments. Additionally, adaptive acquisition enables kilohertz-rate partial readouts, exceeding full-array raster scanning on the same sensor by 19.2X at 16 sampled taxels per update for a 1024-taxel skin. We provide all hardware, software, and result videos are at opentac.github.io
Figures & tables
Figure 1: Tactile samples recorded with OpenTac skins. Heatmaps indicate normal load; cyan arrows denote per-bump shear; white arcs denote rotation estimates. (a) 64×64 skin, twisting a dome and clippers. (b) 9×9 fingertip, opposite thumb twists. (c) UMI gripper, 29×6 finger skins: can and ball rocked in the grasp. (d) Extrinsic contact: screwdriver moved down and up.
Figure 2: OpenTac-CAD creates manufacturable tactile skins for 2D/3D shapes. (A) Selected surfaces (orange) on YUBI, UMI, LEAP Hand, Robotiq 2F-85, and Unitree B2 models and the generated FPCBs; labels give taxel count, pitch, and row-plus-column readout channels. (B) (i) Sensor stack: a printed bump lid places one bump over each 2×2 taxel group above the piezoresistive film and two-layer FPCB. (ii) One generated taxel and (iii) a routed corner of the YUBI board; values in orange are compiler inputs, shown at the YUBI settings, that can be changed to generate a different sensor. (C) Sensorized area versus the largest inscribed rectangular array at the same pitch, and generation time versus taxel count for all 31 fabricated boards, all with zero DRC violations.
Figure 3: A. 31 sensors in various form factors designed with OpenTac-CAD and fabricated. All sensors were tested and work. B. We provide two readout board designs, varying in the size and the number of sensor wires they support. Each board contains filtering and readout circuitry, an IMU, and a microcontroller. It can be plugged directly into a USB port to stream data.
Figure 4: OpenTac-Sim enables the simulation of OpenTac sensors, ensuring that normal and shear forces are accurately modeled. We use an FEM model that reads the sensor manifest created by OpenTac-CAD and supports the simulation of sensors of arbitrary shapes and resolutions. A. FEM model uses a tetrahedral mesh to simulate bump deformation and computes normal and shear forces using Eq. 1 . B. OpenTac-Sim includes support for multiple robots from the MuJoCo menagerie, across several contact-rich tasks.
Figure 5: Tactile skins from one-sentence language descriptions. A coding agent (Claude Opus 5) generated each board design from the quoted prompt using OpenTac-CAD and web search. (A) Logged decisions for an Allegro Hand fingertip: a dimension looked up online, an assumption about the tip geometry, and a pitch change forced by connector width. (B) Further designs; the bottom row used outline-growth mode at the operator’s request. Labels give taxel count, routed row-plus-column nets, and board size. These examples evaluate automated design generation, not fabrication.
Figure 6: Sensor calibration. (a–c) Per-taxel response to normal loading against a load cell: median and interquartile range per force bin; diamonds mark saturation onset (5% of pressed taxels at full scale); dashed lines mark the 12-bit ADC limit. (a) Three films (2.0 mm pitch, 0.2 mm gap). (b) Electrode gaps of 0.1–0.5 mm (LINQSTAT, 2.0 mm pitch). (c) Pitches of 1.5, 2.0, and 4.0 mm (LINQSTAT, 0.2 mm gap). (d) In-sample three-axis calibration of a bumped 4×4 patch against an OptoForce reference; the dashed line is y=x , and legends give R2 and RMSE.
Figure 7: OpenTac-Sim faithfully simulates our shear sensor. We collect data by pressing the bump strip on top of a calibrated sensor with a force-controlled UR5e robot with forces ranging from 1 N to 5 N. We reproduce the forces in simulator and compare alignment between simulation vs real-world force readings and contact depths.
Figure 8: SO-101 peg insertion with deformable tactile pads. Blue spheres and orange cubes mark training ( n=100 ) and held-out evaluation ( n=50 ) start poses, respectively. The purple curve shows an expert trajectory, with translucent gripper configurations at multiple timestamps; the dashed gray curve shows an alternative demonstration. Inset shows close-ups of the pads during insertion. Arrows show per-bump tangential displacement.
Figure 9: Robot deployments. OpenTac sensors deployed on four physical robot platforms to support policy learning, contact-rich control, and interactive applications. A. Plug-in-socket insertion on an SO-101 robot arm where shear sensing is central to task success. B. Key-in-lock insertion using a custom-designed cylindrical locking mechanism. C. Rolling a ball using a shear-controller on the UR5e robot. D. Interactive impedance control on the Dexmate Vega-1 Pro robot. Tactile skin on the robot’s forearm senses and moves in the direction of a push.
Figure 10: Tactile policy learning: A. Peg-in-hole and Plug-in-socket insertion on an SO-101 robot arm. Success rates are reported over 50 trials for each condition. “Pose” uses joint angles only; “full” adds tactile observations; neither uses vision. OpenTac-1D senses normal pressure; OpenTac-3D adds shear. We evaluate FlexiTac [ 1 ] as a baseline normal force sensor. To account for each sensor resulting in slightly different grip friction (e.g., due to bump strips) and demonstration distributions, we train and evaluate a proprioception-only variant for each sensor as a baseline. We use compact letter display (CLD), based on statistical significance testing protocols from [ 38 ] . Two violins that do not share a letter are (statistically) significantly different ( >90% confidence interval). B. Key-in-lock insertion task on bimanual YAM robot arms. Success rates are reported across 25 trials. Across all tasks, shear-sensing variants (OpenTac-3D) achieve higher success rates.
Figure 11: Predicting tactile contact across skins and from a reduced foam model. (a) Median-performing presses at ≥4 N; rows are target ← source at a shared physical scale (bar: 10 mm). Predicted, simulated, and measured panels share the target’s peak scale; sources use their own. (b) Median shape F1 by object; grey marks same-sensor repeatability. (c) F1 versus calibrated force. (d) Transfer F1 for each ordered skin pair on co-visible support. All predictors receive contact pose. Simulation covers printed indenters only; Robotiq simulation exemplars are in-sample and excluded from aggregates.
Figure 12: Adaptive partial scanning. (a) Frame rate versus per-frame sampling budget M on a 32×32 skin; full raster scanning reads all 1024 taxels at 67 frames/s. (b) Frames capturing a tennis-ball impact from a 30 cm drop (50% full-scale threshold). Open circles are trials and filled circles means; the dashed curve gives the frames spanned by a 3 ms contact at each frame rate; detection counts appear above. (c) Representative impact at M=128 . (d) Firmware sampling patterns at M=64 on four skin shapes (not to scale). Color is ADC counts above baseline; black taxels were sampled without contact, and blue taxels were not sampled.
Feedback
Held ↑
Slip (cm) ↓
No load
Proprioception only
3
—
F/T sensor
20
0.15±0.08
OpenTac-1D
20
0.2±0.1
OpenTac-3D
20
0.11±0.06
Load
Table 2: Hold stability on a Dexmate Vega-1 Pro. Twenty trials per condition, Slip is reported as mean ± std.; Held counts trials reaching 40 s. — dropped, so slip is undefined.
Figure 13: Sensor endurance testing : We continuously indent a bump on a sensor with a robot end effector for 10 hours. Every 30 minutes, we log sensor readings for forces in the range 3 N - 5 N. We find that after the first 2 hours of interaction, the readings stabilize, and maintain a clear relative separation throughout the 10-hour load-cycle test.
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/.
A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale. Human demonstrations are far cheaper to collect and offer a path to scale this data, but only if human and robot hands carry tactile sensors with corresponding signals. This requires sensors that conform to different hand geometries, cover the full hand, and share a common layout across embodiments. We present SoTa, a low-cost capacitive tactile skin that provides full-hand coverage on humans and robots while preserving a shared layout of 202 taxels across corresponding finger and palm regions. Our multilayer design with fabric electrodes enables in-house fabrication of thin, soft skins with customizable geometry for under $10 in materials per skin. The sensor retains over 97% of its initial response span after 10,000 loading-unloading cycles with traces retaining continuity through 1,280 tight-fist folding cycles. The shared taxel layout supports human-robot co-training with a common tactile encoder and no learned cross-sensor mapping. Across three contact-rich manipulation tasks, tactile observations improve in-distribution success over vision-only policies. With a fixed robot demonstration budget, adding human demonstrations more than doubles mean success across eight evaluation conditions, from 22.8% to 45.9%, improving success in all five out-of-distribution conditions. We plan to open-source the resources needed to fabricate and operate these skins.
Jingyun Yang, Baiyu Shi, Timothy Yu +6
Stanford University · Tsinghua University · University of Cambridge
Vision-based tactile sensing is widely used in perception, reconstruction, and robotic manipulation, yet collecting large-scale tactile data remains costly due to diverse sensor-object interactions and inconsistencies across sensor instances. Existing approaches to scaling tactile data---simulation and free-form tactile generation---often yield unrealistically rendered signals with poor transfer to highly dynamic real-world tasks. We propose \name, a two-stage controllable tactile image generation framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact pose. By grounding generation in these important physical priors, \name synthesizes realistic samples across different sensors while effectively capturing task-relevant variations. Across a series of downstream tasks and real-world experiments, such as object insertion, imitation learning, and object weighting, the augmented datasets using our approach consistently improve performance and demonstrate practical utility in dynamic real-world settings. Project page: https://dongyuluo.github.io/controltac.
Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh +3
The University of Hong Kong · University of Maryland · University of Maryland, College Park