Tactile Sensing

Follow this topic Ask about this topic

Latest papers 113

26 papers in the last four weeks, up 136% on the four weeks before. 0.2% of all new papers.

Jun 30, 2026cs.LG

Making Sense of Touch from the Child's View for Contrastive Learning

Is the sense of touch a mechanism for human babies' learning of visual concepts? If so, can we quantify its importance, and to what extent do babies rely on their sense of touch for visual learning? To approach these questions in a principled way, we propose a structured coding system for baby-centric touch events, yielding a dataset of 264k two-second clips of touch events coded according to this system. Using this dataset, we pretrain developmentally grounded models that reveal promising insights into the nature of baby learning from touch.

Max Whitton, Zecheng Wang, Puchen Liu +12

Boston University · University of Texas, Austin

Tactile Sensing Visuo-Tactile Representation Learning

Jun 30, 2026cs.RO

UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation

Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this paradigm to the tactile domain, where both object-level semantics and sensor-level configurations jointly determine the meaning of touch. To address this gap, we propose UniTac, the first UMM designed for tactile understanding and generation. UniTac models the tactile process as a transition from non-contact to contact, capturing the physical interaction between sensors and objects through a dual-level representation that encodes both sensor and object attributes. For tactile understanding, UniTac introduces two tasks, object property description and sensor identification, to enhance reasoning over physical and cross-sensor information. For tactile generation, we design a two-stage training paradigm consisting of reconstruction and alignment, together with a sensor-prior-based sampling strategy that simulates realistic tactile contact. Trained on large-scale multi-sensor datasets, UniTac achieves state-of-the-art performance in tactile understanding and generates realistic tactile signals across sensors.

Jiahang Tu, Fengyu Yang, Chenyang Ma +8

Zhejiang University · Yale University · UNIX AI +3

Tactile Sensing Unified Multimodal Models

Jun 25, 2026cs.RO

VibeAct: Vibration to Actions for Contact-Rich Reactive Robot Dexterity

Dexterous manipulation depends on contact events that are fast, local, and often visually occluded. Piezoelectric microphones offer a compact and high-bandwidth way to sense these interactions, but the resulting vibro-acoustic signals are difficult to simulate faithfully enough for end-to-end sim-to-real policy learning on dexterous robot hands. We propose VibeAct, a framework that bridges real vibrotactile sensing and simulation-based reinforcement learning through a shared physical representation of contact and slip. In the real world, we embed piezoelectric microphones into a dexterous robot hand and collect vibro-acoustic data through teleoperation, then replay the recordings in a calibrated digital clone to automatically label per-finger contact and slip. A tactile estimator learns to predict contact and slip from real microphone waveforms, while manipulation policies are trained in simulation on the same representation computed directly from simulated contacts. This decoupling lets policies exploit rapid tactile feedback without simulating raw audio. Across five contact-rich tasks spanning regrasping, in-hand reorientation, and insertion, VibeAct consistently outperforms a proprioception-and-point-cloud baseline in simulation, with the largest gains on tasks requiring sustained reactive control, where the continuous slip-magnitude channel proves the most informative observation. The learned policies transfer to a physical dexterous hand-arm platform, improving success rates on deployed tasks. Project videos and additional details are at https://vibeact.github.io/.

Yuemin Mao, Uksang Yoo, Jean Oh +2

1Carnegie Mellon University · 2Bosch Center for Artificial Intelligence

Tactile Sensing Contact-Rich Robotic Manipulation Sim-to-Real Transfer Robotic RL Robotic Manipulation Tactile Sensing for Robotic Manipulation

Jun 24, 2026cs.RO

A Sensorised Lattice Footplate for a Semi-Active Prosthetic Foot

This paper investigates whether magnetic plantar sensing can be embedded directly inside the load-bearing compliant element of a low-cost semi-active prosthetic foot. We present a prototype integrating a sensorised 3D-printed lattice footplate, a servo-adjustable hydraulic damper, and a reduced-order ankle model. The damper is experimentally characterised to relate adjustment angle to damping coefficient. Controlled compression tests show tunable lattice stiffness, while cyclic normal loading shows that the embedded sensor tracks the testing-machine reference force, supporting plantar-force estimation without an external insole layer. Static-posture trials under approximately body-weight loading show that forefoot and rearfoot loading distributions are separable across four prescribed stance configurations, providing a preliminary check of the sensing pipeline. A feedforward damping schedule approximates the dorsiflexion trend of a reference ankle trajectory through early-to-mid stance, while exposing the expected limitation that a purely dissipative mechanism cannot generate active push-off. Together, these results demonstrate that sensing can be embedded inside the load-bearing compliant element of a prosthetic foot and used to drive semi-active damping.

Jinze Ge, Jingcheng Sun, Chengxu Zhou

Dept. of Mechanical Engineering, University College London, UK · Dept. of Computer Science, University College London, UK

Tactile Sensing Assistive Robotics

Jun 24, 2026cs.RO

A 3D-Printable Dataset for Fair Testing and Comparisons of Tactile Sensors

Existing texture datasets for tactile sensing primarily consist of sensor readings from a specific sensor interacting with available surfaces/objects rather than describing the textures themselves, limiting fair comparison between tactile sensors and hindering reproducible research. In this work, we introduce a 3D-printable dataset of mathematically defined textures designed to be fabricated reliably across different printers and filament types. The dataset consists of six parametrically generated surface patterns derived from combinations of sine-wave and Fourier-based functions, giving controlled variation in spatial frequency, amplitude, and directional structure. We evaluate the reproducibility of these textures across three popular 3D printers and multiple filament types by measuring variance in images captured using an optical TacTip sensor under controlled contact conditions. Our results show that print quality, particularly peak sharpness and stringing, affects tactile variance, with higher-end printers producing significantly more consistent signatures. Classification experiments using neural networks and PCA-based models further demonstrate that high-quality prints support strong within-printer generalisation, while cross-printer generalisation remains challenging due to geometric inconsistencies. This work establishes the first openly available, physically reproducible 3D-printed texture benchmark, providing a foundation for fair comparison of tactile sensors.

Dexter R. Shepherd, Nicolas Herzig, Phil Husbands +3

University of Sussex, Department of Informatics · University of Sussex, Department of Engineering

Tactile Sensing

Jun 24, 2026cs.RO

Self Capacitive Tactile Sensor System designed for Companion Robots

Tactile sensing is essential for humanoid robots to achieve safe physical interaction, dexterous manipulation, and truly human-like responsiveness. However, the design of such systems remains challenging. Conventional approaches often suffer from complex multilayer structures, intricate wiring, high cost, and poor scalability, making it difficult to realize full-body tactile sensing with real-time, low-latency detection while maintaining minimal computational load on the robot's main processor. In this work, we present a simple, scalable and hardware friendly tactile sensing system for a companion humanoid robot based on the self-capacitance principle. The proposed sensor system employs a single conductive fabric layer with a conductive fabric wire architecture and does not require intricate electrode patterning. Scalability was demonstrated by fabricating a 100-point sensor array on a flexible printed circuit (FPC). Evaluation across sampling frequencies showed that 10 Hz is insufficient and misses transient events, whereas 100 Hz and 1000 Hz reliably capture and clearly distinguish all interaction types: gentle touch, slow tapping, fast tapping, and hitting. A decision-tree classifier was implemented directly on the FPGA, offloading real-time inference from the Raspberry Pi 4 with minimal latency and negligible power overhead. This design fully meets the tactile sensing requirements of the HIRO-chan robot and is well-suited for full-body tactile sensing in HIRO-chan and other companion robots.

Mohsin Ali, Hidenobu Sumioka, Shuhei Ikemoto

Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196 Japan · Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto 619-0288 Japan

Tactile Sensing Robotic Tactile Sensing Humanoid Robotics

Jun 23, 2026cs.RO

SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots

Legged robots rely on accurate ground interaction awareness to traverse variable terrains, such as slippery surfaces. Existing slip detection methods often rely on kinematics and proprioception, which lack the sensitivity to detect early-stage slips that occur prior to catastrophic instability. Thus, this paper presents SlipSense, a novel framework for online force-based slip detection using a custom lightweight sensorized foot for quadrupeds to detect slip. The framework integrates a multimodal sensor design with a LSTM-based model to infer ground reaction forces and detect slip-indicative anomalies during locomotion. The proposed framework is deployed on a Unitree Go1 quadruped to demonstrate blind online slip detection over a slippery terrain. Our method detects early-stage slips down to an average displacement of 24.1 +/-6.4mm with an overall accuracy of 85.9%. This represents a 3.3-fold finer detection resolution and a 24% relative accuracy improvement over a standard kinematic baseline that uses foot velocity inferred through state estimation. The work in this paper serves as a foundation for force-aware gait adaptation in legged robotic locomotion, allowing future controllers to estimate terrain friction and adjust constraints, thus improving the overall stability of the system.

Iris Szu-Yao Liu, Chien Chern Cheah, Meng Yee Michael Chuah

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Institute for Infocomm Research, Agency for Science Technology and Research, Singapore

Tactile Sensing Robotic Tactile Sensing Quadruped Locomotion Multimodal Sensor Fusion

Jun 20, 2026cs.RO

Geometric Reconstruction of Extrinsic Contact Trajectories using Tactile Sensing and Proprioception for Tool Manipulation

Tactile sensing enables robots to perceive rich contact information at the grasp, supporting tasks such as object recognition, in-hand pose estimation, and slip detection. However, in many tool-mediated manipulation tasks, the interaction that determines task success occurs at the tool tip, away from the tactile sensor, making direct sensing of tool-environment contact difficult, particularly when the contact moves during interaction. In this work, we reconstruct the trajectory of extrinsic tool-tip contact using tactile sensing and robot proprioception. We formulate tool-tip trajectory reconstruction as a geometric inference problem under a single-point contact assumption. Our method first estimates the global tool-tip contact location from a calibration segment designed to approximate fixed-point behavior, and then reconstructs the full trajectory by composing relative tool motion estimated from tactile marker observations under continuous contact. Across n=51 trials with multiple trajectories, tools, wrist poses, and grasp configurations, the proposed pipeline achieves a trajectory RMSE of 8.59 +/- 2.41 mm in the world frame and a shape RMSE of 5.96 +/- 1.16 mm, while operating online at 14.00 +/- 4.11 Hz. Overall, the results show that extrinsic tool-tip trajectory geometry can be recovered consistently from grasp-level tactile sensing, with trajectory shape remaining stable across variations in tools, wrist poses, and grasp configurations.

Seojung Min, Yoonjin Kim, Jeong-Jung Kim +1

Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea · Department of AI Machinery, Korea Institute of Machinery and Materials, Daejeon 34141, Republic of Korea

Tactile Sensing Robotic Tool Use Robotic Perception

Jun 18, 2026cs.RO

TaCauchy: An Extensible FEM Framework for Vision-Based Tactile Simulation

Vision-based tactile sensors require high-fidelity simulation for reinforcement learning, yet existing approaches struggle to provide accurate mechanical stress fields within GPU-accelerated robotics platforms. We present TaCauchy, an extensible Finite Element Method (FEM) framework that integrates rigorous physics-based force computation into Isaac Sim. Built on the Unified Incremental Potential Contact (UIPC) solver, TaCauchy directly computes Cauchy stress tensors from hyperelastic constitutive laws and projects them onto contact surfaces to obtain traction forces and pressure distributions, providing mechanical ground truth from first principles rather than empirical estimation. Our framework features automatic mesh generation with geometry-aware adaptive refinement and a modular sensor interface enabling rapid integration of diverse sensors (GelSight Mini, DIGIT, 9DTact) with minimal configuration. Performance benchmarks demonstrate 33.40 FPS for single environments and 555 FPS aggregate throughput across 60 parallel environments, with stress extraction overhead under 1 ms. Physical validation experiments show strong agreement between simulated and real tactile responses across force ranges from 1.2556 N to 4.7332 N, achieving SSIM above 0.93, confirming the framework's capability to provide accurate, physically-grounded force supervision for downstream robotic manipulation tasks.

Hengfei Zhao, Yifan Xie, Junhao Gong +6

Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Huawei Inc., Shenzhen, China

Vision-Based Tactile Sensing Tactile Sensing Robotics Simulation Finite Element Method Robotic Tactile Sensing Deformable Object Simulation

Jun 17, 2026cs.RO

TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer

Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation. However, current simulators lack the fidelity to faithfully model the complex deformation and transduction mechanics of tactile sensors, severely hindering sim-to-real transfer in robot learning pipelines. To address this challenge, we propose a multi-modal representation learning framework that aligns heterogeneous tactile modalities within a shared latent space, eliminating the need for accurate raw-signal simulation while preserving relevant contact information. Our approach employs modality-specific encoders to project diverse tactile observations, such as simulated penetration depth and real-world capacitance, into a common embedding space. The model is trained using self- and cross-reconstruction objectives alongside contrastive alignment, encouraging modality-invariant yet information-rich representations. We evaluate the learned embeddings on indenter shape identification, force prediction, and geometric reconstruction tasks, training exclusively in simulation and testing directly on real sensor measurements. Our results demonstrate zero-shot sim-to-real transfer across physically dissimilar representations. Furthermore, incorporating multi-physics simulation modalities yields more informative embeddings that transfer across diverse downstream tasks, demonstrating a 16.7% reduction in force prediction error and a 45.8% reduction in shape reconstruction error. Finally, we release an efficient Warp-based implementation of a penalty-based tactile simulation model for Isaac Lab, enabling scalable tactile data generation.

Arunim Joarder, Arjun Bhardwaj, René Zurbrügg +6

Robotic Systems Lab, ETH Zürich · ETH AI Center · NVIDIA +1

Cross-Modal Representation Learning Tactile Sensing Robotics Simulation Sim-to-Real Transfer Multimodal Contrastive Learning

Jun 16, 2026cs.CV

Contact-Based Fringe Projection Profilometry for High-Resolution 3-D Surface Measurement of Reflective and Transparent Objects

This paper presents a contact-based 3-D surface measurement method based on a Digital Fringe Projection (DFP) system, belonging to the vision-based tactile sensing family pioneered by the commercially successful GelSight sensor. Such sensors have proven effective for robotic fingertip manipulation and contact sensing. However, because GelSight employs photometric stereo with RGB LEDs, it does not measure absolute depth directly but instead infers it by integrating estimated surface gradients, which can accumulate reconstruction errors; in addition, it becomes increasingly difficult to calibrate as the sensing area grows, and its depth accuracy is challenged on highly reflective or transparent objects. To overcome these drawbacks, we propose a fringe-projection-based contact measurement technique that performs triangulation-based 3-D reconstruction on a coated silicone contact surface, providing dense per-pixel surface geometry and full-field 3-D shape measurement over the contact region. By integrating high-accuracy digital fringe projection into the sensor, our approach simplifies calibration over larger areas and enhances depth precision for complex surfaces. Experimental results, including a direct comparison with a GelSight Mini sensor, a sphere-fitting accuracy evaluation, and an uncertainty analysis, confirm that the proposed method significantly improves the accuracy and stability of structured-light-based 3-D measurements, allowing reliable reconstruction of objects with diverse optical properties.

Ingu Yeo, Hyung-Gun Chi, Jae-Sang Hyun

Department of Mechanical Engineering and the Yonsei Institute for Embodied Intelligence, Yonsei University, Seoul 03722, South Korea

Fringe Projection Profilometry Tactile Sensing 3D Reconstruction Tactile Sensing for Robotic Manipulation

Jun 15, 2026cs.RO

ART-Glove: Articulated Tactile Glove for Contact-Grounded Dexterous Interaction Capture

We present ART-Glove, an articulated tactile glove designed to capture contact-grounded dexterous demonstrations while preserving human dexterity. ART-Glove makes hand-side contact geometry explicit with 16 rigid functional surfaces covering the fingers, thumb, and palm. Twenty-two anatomically aligned joints connect these surfaces and allow them to follow human hand motion during dexterous manipulation. Encoder-based sensing tracks surface motion, while dense piezoresistive tactile sensing records contact over the same surfaces. The complete system captures synchronized 22-DoF joint measurements and 2048-taxel tactile measurements at 120 Hz. We evaluate ART-Glove across experiments on motion freedom, joint sensing, tactile sensing, and contact-rich interaction capture, demonstrating its ability to preserve human dexterity while recording contact-grounded information that can support downstream dexterous robot learning.

Changyi Lin, Ding Zhao

Carnegie Mellon University

Dexterous Manipulation Tactile Sensing Wearable Sensing

Jun 14, 2026cs.RO

GeoTLM: Geometry-aware Tactile-Language Models for Contact Motion Orientation Reasoning of Dynamic Objects

Modern tactile-language models (TLMs) have shown potential for robot learning tasks, such as material and texture recognition. However, for contact-rich scenarios, these TLMs struggle to understand the physical properties of dynamic objects, such as rotation and sliding directions. For instance, our preliminary experiments reveal that popular TLMs, such as Sparsh and AnyTouch2, exhibit weak performance on basic rotation direction reasoning from GelSight Mini tactile data. This surprising gap inspires us to explore a novel research question: Can we inject physically grounded geometric priors into TLMs to enable reliable contact orientation reasoning of dynamic object properties? To this end, we propose GeoTLM, a novel geometric representation-guided TLM for the perception of dynamic contact events. Our key idea is to preserve and structure tactile shear-field geometry before language-level reasoning, rather than forcing low-resolution tactile tokens into fragile closed-form physics operators. To achieve this, we propose a lightweight (only 14k parameters) yet novel Differentiable Geometric Representation (DGR). Specifically, DGR learns a contact-mask-guided representation in the shear field and aggregates it through an antisymmetric seven-region pooling design, motivated by the physical intuition that rotational contact produces antisymmetric deformation patterns. We conduct experiments on two representative tasks: rotation direction and sliding direction reasoning. Extensive experiments show that GeoTLM improves novel-object rotation accuracy by +14.6% and real-sensor sliding accuracy by +16.2% over the same backbone without the geometric encoder. Overall, our work paves a new way for physically grounded tactile-language reasoning, with strong potential for dynamic object understanding and contact-rich robotic manipulation.

Qiutian Li, Zinan Liu, Lin Wang

School of EEE, Nanyang Technological University (NTU), Singapore

Tactile Sensing Contact-Rich Robotic Manipulation Geometric Representation Learning Robotic Tactile Sensing

Jun 12, 2026cs.LG

More with LESS -- Local Scene Representations for Tactile Imaging

Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation. Recent self-supervised learning approaches have shown promising results, but rely on global, unstructured representations and robot-controlled sensing, limiting generalization and practical use. We propose Local Encoder for Spatial Sensing (LESS), an object-centric tactile representation that exploits the local nature of touch. The tactile scene is modeled as a grid of recurrent encoders with local receptive fields, whose states are fused to reconstruct 2D or 3D images of internal structure. This compositional design enables strong generalization: models trained on single-inclusion phantoms accurately image objects with multiple inclusions and varying sizes. The local structure further supports spatial uncertainty estimation. In addition, we enable hand-held tactile imaging via external pose tracking and human-like palpation data, and extend tactile imaging to full 3D reconstruction.

Zohar Rimon, Elisei Shafer, Tal Tepper +4

1Technion - Israel Institute of Technology · 2Rambam Health Care Campus

Tactile Sensing 3D Reconstruction Image Reconstruction Object-Centric Representation Learning

Jun 11, 2026cs.RO

Scalable Dynamic Tactile Sensing Enabled by Passive and Flexible Acoustic Waveguides

Artificial dynamic tactile sensing requires sensitivity, robustness, and compliance, yet existing technologies face trade-offs when scaling to large-area arrays, compounded by wiring complexity and cost. Here, we report a passive distributed paradigm using deep sub-wavelength acoustic waveguides that decouples performance from structural flexibility. Elastic-membrane-capped Helmholtz resonators interconnected by spring-reinforced microtubes form an enclosed network with invariant acoustic transmission under macroscopic bending. By sparsely embedding microphones, the system achieves real-time localization (4 mm highest spatial resolution; >99% accuracy in a 4 microphones 64-node sensing array) and waveform reconstruction of low-frequency signals (<100 Hz). Fast Continuous Wavelet Transform and a lightweight neural network enable inference within 5.5 ms. We demonstrate conformable prototypes-fingertip arrays, a tactile glove, and large-area skins-detecting stimuli from single-hair contact to 5-mg particle impacts, arterial pulse waves, feather touches, and finger contact. This establishes a scalable, flexible, low-cost paradigm for next-generation human-machine interfaces.

Guimin Long, Changhong Linghu, Chuanping Liu +2

Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China

Tactile Sensing Wearable Sensing

Jun 11, 2026cs.RO

Low cost, easily manufactured, highly flexible strain and touch sensitive fiber for robotics applications

Existing stretch and touch sensors for robots are generally expensive with respect to at least one of material costs, required manufacturing equipment, or manufacturing time. We present and experimentally characterize a conductive fiber made using only inexpensive commercial off-the-shelf parts (conductive thread at

0.07/ft, silicone tubing at

0.94/ft) and tools (loop-style needle threader at $2), which can be manufactured quickly (20 cm length in 2 minutes.) We demonstrate its use as a resistive strain sensor with three applications: Triggering a grasp in a pneumatically actuated assistive finger, sensing the pose of a pneumatically actuated robotic strap, and estimating the pose of a flexible solid. We also demonstrate that it can be used as a capacitive sensor with two applications: First, as a touch sensor which triggers a commercial robot arm to move, and second, as a near-field sensor enabling the robot arm to follow a moving hand. The capacitive sensors are knitted, showcasing the high flexibility of the fiber. We discuss methods for improving manufacturing scalability and their cost trade-offs. Finally, we demonstrate a method for repairing a cut fiber.

Christian Diaz Herrera, Srushti Raste, Simin Liu +10

Patton · Wesleyan University · Computer Science, Department of Mathematics and Computer Science +1

Tactile Sensing Robotic Tactile Sensing

Jun 10, 2026cs.CV

Tac-DINO: Learning Vision-Tactile Features with Patch Alignment

Touch is the primary medium through which humans interact with the environment. Currently, tactile learning mainly focuses on image-level pretraining or alignment. However, tactile signals correspond to local object contact, while research into scale alignment and holographic matching remains limited and proper datasets and benchmarks also lack. To bridge this gap, we first construct a data collection system to acquire a large-scale tactile dataset, with over 20 K tactile contacts from 505 real-world objects. Building on this dataset, we design a Vis-Tac Holographic Matching Benchmark to evaluate vision-tactile local-to-global alignment ability. Then we propose Vision-Tactile Patch Alignment (VTPA) methods for vision-tactile representation learning. Experiments demonstrate that these exceed the performance of methods without alignment and align with whole-object images.

Hong Li, Yankang Dong, Yue Xu +8

Shanghai Jiao Tong University · Ant Group · Shanghai Innovation Institute

Representation Learning Tactile Sensing Visuo-Tactile Representation Learning Cross-Modal Alignment

Jun 10, 2026cs.RO

Deformable In-Hand Slip-Aware Tactile Sensor with Integrated Velocity Sensing, Force/Torque and Pressure Map Estimation

This paper introduces a novel tactile sensor for in-hand manipulation with slip-aware control that integrates velocity and force/torque sensing with pressure map estimation into a single device with a deformable contact pad. To the best of our knowledge, this is the first sensor to combine these sensing modalities within a single compliant structure. The sensor features a deformable contact surface and can robustly track both flat and curved surfaces across a wide range of diffuse surface materials. Its performance is evaluated through a comprehensive set of experiments that highlight both its capabilities and limitations. The sensor is designed for rapid and low-cost fabrication using a combination of standard PCB manufacturing and rapid prototyping techniques.

Gabriel Arslan Waltersson, Yiannis Karayiannidis

Department of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden · Department of Automatic Control, Lund University, Sweden

Tactile Sensing Tactile Sensing for Robotic Manipulation

Jun 9, 2026cs.RO

HiPi: Reproducible High-Fidelity Piezoresistive Sensors for Robotic Manipulation

Piezoresistive tactile sensors are attractive for robotic manipulation because they are thin, lightweight, low-cost, and scalable to dense large-area sensing. However, existing systems still face a practical trade-off: recent reproducible designs emphasize accessibility and ease of reproduction, whereas high-fidelity readout architectures remain more difficult to fabricate, assemble, and deploy. We present HiPi, a reproducible high-fidelity piezoresistive sensing system for robotic manipulation. Building on a low-crosstalk readout principle, HiPi redesigns the complete hardware stack around reproducibility, deployability, and multi-sensor scalability. The system includes a compact readout PCB compatible with commercial PCB fabrication and assembly services, eliminating manual soldering; a smaller and lower-cost STM32-based MCU module; an optimized communication pipeline that achieves 220 Hz readout in a bimanual setup with four dense tactile arrays (2048 taxels in total); and FPCB-based conductive layers that simplify sensor fabrication and stacking. Experiments with structured 3D-printed contact patterns show that HiPi preserves contact geometry substantially better than a reproducible baseline, improving the average IoU from 0.428 to 0.797 and the average Dice score from 0.539 to 0.886. These results suggest that HiPi bridges an important gap between reproducible fabrication and high-fidelity readout, making dense piezoresistive tactile sensing more practical for bimanual manipulation and multi-fingered robotic systems.

Changyi Lin, Raihan Haque, Hui-Ping Wang +1

Carnegie Mellon University · General Motors

Tactile Sensing Robotic Tactile Sensing

Jun 8, 2026cs.RO

Dense Force Estimation with an Event-based Optical Tactile Sensor

Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation. While vision-based tactile sensors enable dense force estimation, they are limited by camera frame rates, motion blur, and data bandwidth. Event-based optical tactile sensors offer an attractive alternative with microsecond temporal resolution and low motion blur, but existing methods are restricted to predicting only net forces. We introduce the first framework for dense 3D force field reconstruction using event-based optical tactile sensors. Our approach estimates 3D surface displacements from event data and maps them to forces via the inverse Finite Elements Method (iFEM). Shear displacements are recovered through the proposed event-based marker tracking algorithm, while normal displacements are predicted by a convolutional neural network trained on a collected dataset of synchronized force-displacement-event data. Experiments demonstrate accurate reconstruction of physically grounded forces, achieving a mean absolute error of (0.14 N, 0.10 N, 0.93 N) over force ranges up to (4 N, 4 N, 20 N), while operating at an average of 100 Hz. This work constitutes a first step toward enabling dense force feedback for high-frequency control in robotic grasping and dexterous manipulation.

Agis Politis, René Zurbrügg, Valentina Cavinato

Sony Advanced Visual Sensing, Zurich, Switzerland · Computer Vision and Geometry Group at ETH Zürich

Tactile Sensing Robotic Tactile Sensing

Jun 8, 2026cs.CV

EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video

Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware. Existing vision-based methods predominantly rely on planar surfaces or fingertip contacts, failing to generalize to complex 3D object interactions. Therefore, we introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects, incorporating a bare-hand transfer subset to enable generalization to natural scenarios. Leveraging this benchmark, we first establish EgoPressureFormer as a discriminative baseline. Beyond this, to explicitly address the uncertainty in partial observations, we propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone. By combining rich world knowledge priors with a Physically-Informed Feature Rectification layer to inject semantic constraints, our approach effectively infers plausible contact patterns and resolves visual-physical ambiguities. Extensive experiments demonstrate that our method achieves superior performance on the benchmark and robust transferability to in-the-wild scenarios. Our project page is available at https://egotactile.github.io/.

Yuan Zeng, Yujia Shi, Tiao Tan +6

Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China · Department of Network, Pengcheng Laboratory, Shenzhen, China +2

Vision-Based Tactile Sensing Video Diffusion Models Tactile Sensing Tactile Sensing for Robotic Manipulation

Jun 5, 2026cs.RO

Compliance-Based Sensor Placement for Force Sensing on a Sensorized Prostate Phantom

This work presents a compliance-based sensor placement method for force sensing on a sensorized prostate phantom designed for Digital Rectal Examination training. The phantom combines three internal pneumatic chambers, used as intrinsic pressure sensors, with ten surface displacement markers. A finite-element simulation dataset is generated by applying external forces at sampled surface locations, from which a compliance matrix relating force inputs to pressure and displacement responses is constructed. Based on this matrix, we propose a weighted greedy selection strategy that maximizes local force reconstructability while prioritizing the clinically relevant posterior contact region and avoiding marker placement directly within the Region of Interest. Compared with a global QR-based placement strategy, the proposed method increases the mean reconstructability score in the target region by 22.5%. These results suggest that region-aware sparse sensor placement can improve force observability in soft robotic medical phantoms while maintaining a limited and practical sensing configuration.

Sizhe Tian, Yinoussa Adagolodjo, Jeremie Dequidt

CRIStAL, DEFROST · Inria, CNRS, Centrale Lille, UMR 9189 CRIStAL, University of Lille, Lille, France. · CRIStAL, Polytech Lille, DEFROST

Tactile Sensing Soft Robotics

Jun 5, 2026cs.CV

EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation

Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing methods often discretize pressure signal and process frames independently, leading to quantization errors and temporal inconsistencies. We present \emph{EgoPressDiff}, a conditional video diffusion framework that generates UV-pressure maps from visual input. The core of our approach is a multi-modal conditioning strategy, introducing a PoseNet and a Vertex Encoder to efficiently extract features from hand pose and 3D mesh vertices. These signals, along with depth information, guide the generative process to ensure the pressure fields are physically grounded. To effectively fuse these heterogeneous features, we further propose a Distribution-Calibrated Spatial Layer, which aligns their statistical properties before combination. Evaluated on the EgoPressure ego-view setting, EgoPressDiff achieves state-of-the-art results, improving Volumetric IoU by over 34% relative to prior baseline, while reducing MAE and maintaining high temporal accuracy. Our project page is at https://egopressdiff.github.io/.

Yuan Zeng, Zilue Gao, Yujia Shi +3

Shenzhen International Graduate School, Tsinghua University, China · Harbin Institute of Technology, China · Pengcheng Laboratory, China

Video Diffusion Models Tactile Sensing Egocentric Video Understanding Multimodal Diffusion Models

Jun 4, 2026cs.RO

Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation

Touch sensing is beneficial for solving a wide variety of manipulation tasks. While there exists a wide range of tactile sensors with different properties, exploiting the fusion of multiple heterogeneous tactile sensors to improve manipulation learning remains underexplored. We present Multi-Resolution Tactile Sensing (MiTaS), a representation framework that leverages multiple tactile sensors operating at different temporal resolutions in order to solve complex contact-rich manipulation tasks. We propose a novel architecture using modality-specific convolutional stems and transformer-based fusion that effectively fuses information from an RGB camera stream, a vision-based GelSight Mini sensor and a high-frequency event-based Evetac sensor. This multi-sensor representation then conditions a flow-matching policy for solving downstream tasks. Experimental results across five contact-rich manipulation tasks demonstrate the effectiveness of multi-resolution tactile features in imitation learning. MiTaS achieves an average success rate of 80 %, while vision-only (31 %) and visual-tactile (54 %) baselines cannot solve the task reliably. Co-training a visuo-tactile model with multi-tactile data boosts performance by over 10 % in certain tasks, without having access to the Evetac sensor during policy evaluation. A detailed sensor-reading and attention analysis reveals the importance of different sensors throughout task execution, validating our multi-resolution tactile sensing approach. Project Page: http://mitas-touch.github.io.

Rickmer Krohn, Erik Helmut, Niklas Funk +3

Interactive Robot Perception & Learning, TU Darmstadt · Hessian AI · Robotics Institute Germany +1

Tactile Sensing Contact-Rich Robotic Manipulation Visuo-Tactile Representation Learning Robot Imitation Learning Tactile Sensing for Robotic Manipulation Multimodal Sensor Fusion

Jun 2, 2026cs.RO

Towards Estimating Normal and Shear Interface Pressures in Prosthetic Sockets via Least Squares and Mechanics Modeling

Prosthetic socket fitting remains largely manual and iterative, and objective fit metrics are still limited. Part of the challenge is the lack of long-term real-life pressure data at the residual limb--socket interface. Traditional pressure sensors are prone to drift over time, and capture only normal pressures at sparse locations within the socket, missing a critical component for biomechanical analysis: shear. Although some sensors can report both normal and shear interface stresses, these components are often difficult to decouple because of measurement crosstalk. One potential path forward is to develop models that can augment available measurements. This work introduces a testbed to evaluate model performance under sparse pressure sensing using two complementary validation signals: (i) the global wrench (\ie, total forces and moments expressed in an orthonormal frame) transmitted through the socket, by an artificial residual-limb, and (ii) local interface loads (\ie, decoupled normal and shear pressure components in a right-hand-rule orthogonal frame that lives in each instrumented location) measured by sparse sensing clusters, each composed of four capacitance-sensing channels. Rather than presenting full-field pressure estimates, the focus is on an analysis sequence that quantifies how well candidate mechanical models explain both global and local measurements under controlled conditions. A quasi-static spring--mass contact model is evaluated, and its parameters are identified via a two-stage convex least-squares problem. Validation under static loading shows that estimating constant bias terms reduces steady offsets in the wrench channels and improves agreement with local measurements. A Pareto-front sensitivity analysis further illustrates how the trade-off between global and local objectives changes when bias terms are included.

Axel González Cornejo, Tianhao Yu, Chi Hwan Lee +1

Tactile Sensing Model Calibration

Jun 2, 2026cs.RO

Static and Dynamic Representations for Tactile Contact-Angle Estimation with Event-Based Sensors

Event-based tactile sensing offers low-latency signal acquisition for contact-rich robotic interaction. This paper investigates contact-angle estimation using event streams from an event-based tactile sensor (NeuroTac) and compares three event-derived spatial contour representations: a dynamic representation capturing recent event activity, a static representation recovering a more persistent contact state, and their combined representation. Across the evaluated motion scenarios, all representation pipelines exhibited P99 processing latency below 10 ms at all tested sampling intervals, demonstrating their potential for high-frequency event-based tactile angle estimation in robotic manipulation. The static representation consistently achieved marginally better performance than the dynamic and combined representations under scenario-specific training, yielding a mean overall MAE of 0.160° during continuous sensor rolling and a stop-phase mean MAE of 0.251° during randomly inserted motion interruptions. It also exhibited smaller performance fluctuations across speed and indentation depth variations than the other two representations.

Yanhui Lu, Efi Psomopoulou, Benjamin Ward-Cherrier

School of Engineering Mathematics and Technology, University of Bristol, U.K.

Tactile Sensing Robotic Tactile Sensing

May 29, 2026cs.RO

SoFiE: Soft Finger Exoskeleton for Intelligent Grasping

Soft wearable robotic systems have emerged as a promising solution for assisting individuals with reduced hand function. This paper presents SoFiE, a modular soft finger exoskeleton designed to assist index-finger flexion during grasping tasks. The proposed system is primarily fabricated using 3D-printed flexible materials, enabling a lightweight, low-profile, and modular design. Actuation is achieved through a tendon-driven mechanism powered by a compact DC motor, while passive extension is provided by a compliant conductive spring. This element, termed StretchSense, also functions as a proprioceptive sensor by exhibiting resistance changes under deformation. Furthermore, a novel tactile sensing approach, MagSense, is introduced, using a magnet and magnetometer pair embedded in a soft fingertip structure to estimate contact force and object compliance. The system is fully untethered and controlled by an embedded microcontroller. In addition, actuator-level sensing through motor encoder feedback enables estimation of the system state, providing a foundation for safe and adaptive control strategies. Experimental validation demonstrates the capability of the system to provide reliable pose estimation, distinguish between materials with different stiffness, and generate distinct sensor signatures across different grasping tasks. This paper details the design, fabrication, and sensing concepts of the proposed exoskeleton as a proof of concept toward modular, soft, and assistive wearable robotics.

Magnus Malthe Sigsgaard Nielsen, Nicklas Nikolaj Grønvall, Xiaofeng Xiong +1

SDU Soft Robotics, SDU Biorobotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU), 5230 Odense M, Denmark

Robotic Grasping Tactile Sensing Assistive Robotics Soft Robotics

May 29, 2026cs.RO

Haptic Sorter: A Unified Planning Framework for Online Shape Estimation and Real-Time Pose Inference

Robotics manipulation usually assumes that the shape and pose of the object are known to the robot prior to motion planning. However, precise geometric information is not always available in practice, and pose inference suffers from sensor uncertainties and view occlusion. In this work, we propose a unified model-based geometric framework integrating robotic haptic perception, modeling, and manipulation planning. Our novelties involve: \textit{i)} Introducing Bayesian Optimization (BO) to guide the haptic exploration for object shape inference, where superellipses are used to approximate geometric boundary; \textit{ii)} Adaptive formulation of manipulation potential encoding object geometry for quasi-static robot-object interaction; \textit{iii)} Proposing an online Ordinary Differential Equation (ODE) for real-time pose inference based on model prediction and tactile feedback. We deploy our system on a 2D robotic sorting task, and vary object geometries to validate the robustness and generalizability of our framework in both simulation and a real-world multi-arm setup.

Zhuoyi Lu, Lin Yang, Sri Harsha Turlapati +1

School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore

Tactile Sensing Object Pose Estimation Robotic Manipulation Robotic Tactile Sensing

May 28, 2026cs.RO

Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models

Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual representations remains poorly understood. This knowledge gap hinders the integration of touch in digital environments and the development of robots capable of human-like tactile perception. Here, we present an interpretable computational framework for modeling human material perception and recognition using multisensory touch data. Our framework comprises three interconnected models: Model 1 maps finger-surface interaction features to psychophysical sensory attributes, Model 2 classifies materials based on these perceptual representations, and Model 3 directly classifies materials from tactile features. The results showed that combining information from pressing, static contact, and sliding interactions improves prediction accuracy, and that thermal cues are particularly informative for both perceptual modeling and material classification. These findings highlight the importance of thermal and compliance cues, which remain underrepresented in current robotic fingers and haptic displays. Incorporating such cues may enhance artificial systems' ability to approximate human material perception and guide the design of more perceptually grounded haptic interfaces.

Li Zou, Yasemin Vardar

Delft University of Technology (TU Delft), Department of Cognitive Robotics, Delft, 2628 CD, The Netherlands

Tactile Sensing Multimodal Learning

May 27, 2026cs.RO

Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation

A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulation-reality gap prevents information-dense modalities like touch from being effectively used. Existing sim-to-real methods often mitigate this gap by simplifying tactile data into coarse low-dimensional features -- sacrificing the richness required for complex manipulation. In this work, we introduce Center-of-Pressure (CoP), an effective tactile representation grounded in physical principles that preserves dense contact information while maintaining robustness for sim-to-real transfer. To support this representation, we propose a sensor calibration scheme based on differentiable dynamics, enabling the estimation of taxel orientations without requiring ground-truth force measurements. We evaluate CoP on two blind, challenging contact-rich manipulation tasks: peg-in-hole insertion and ball balancing. Across both tasks, policies conditioned on CoP achieve zero-shot sim-to-real transfer on a multi-fingered hand, and outperform both coarse binary-contact and raw-taxel baselines. Analysis of learned policy states further suggests that CoP-conditioned policies encode task-relevant physical properties, such as object mass, as an emergent byproduct of control.

Jiahe Pan, Stelian Coros, Jitendra Malik +1

1ETH Zürich · 2UC Berkeley

RL for Robotics Tactile Sensing Contact-Rich Robotic Manipulation Sim-to-Real Transfer Robotic Manipulation Tactile Sensing for Robotic Manipulation