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Tactile

Publish Date	Title & Abstract	Authors	Links
2026-06-09	TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation Dexterous Manipulation Tactile Contact-rich manipulation requires robots to continuously perceive and regulate evolving physical interactions under dynamic contact transitions or complex surface geometries. Recent imitation learning methods improve contact-aware control by incorporating tactile or force feedback, but they rarely model the asymmetric spatiotemporal roles of global force and local tactile sensing. To address…	Wenchao Ding Team	ArXiv
2026-06-09	IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation Dexterous Manipulation Tactile Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by low-level impedance controllers. In…	Yilun Du Team	ArXiv / Web
2026-06-08	AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning Dexterous Manipulation Tactile Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile and force sensors in handheld or wearable devices. To address these limitations, we first introduce AetheRock for gripper-force, vision, and tactile data collection, which is an arm-worn device featuring a modular and…	Yong-Lu Li Team	ArXiv
2026-06-08	Dense Force Estimation with an Event-based Optical Tactile Sensor Dexterous Tactile 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…	Valentina Cavinato Team	ArXiv
2026-06-08	EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video Tactile 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…	Qingmin Liao Team	ArXiv
2026-06-07	RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation Tactile Effective visuo-tactile integration is critical for robotic dexterous manipulation, especially when visual observations are unreliable or occluded. However, robustly aligning sparse, heterogeneous tactile measurements with dense visual representations remains a fundamental challenge. Most existing approaches require policies to learn cross-modal correspondences implicitly from limited…	Chenxi Xiao Team	ArXiv
2026-06-07	Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation Tactile World action models inherit the predictive capability of world models, enabling action generation to be guided by anticipated future observations. However, they rely primarily on vision and often fail in contact-rich manipulation, where critical cues arise from physical interaction. In this paper, we propose Dream-Tac, a unified Tactile-World Action Model that jointly models actions, future…	Shanghang Zhang Team	ArXiv
2026-06-04	Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation Dexterous Manipulation Tactile 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…	Georgia Chalvatzaki Team	ArXiv
2026-06-04	RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning Manipulation Tactile Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing pipelines either lose deployable dexterity through retargeting or embodiment conversion, or rely on robot-specific teleoperation that is costly to scale and often lacks intuitive, contact-aware control for dexterous data collection. We present…	Zongqing Lu Team	ArXiv
2026-06-04	Visuotactile and Explicitly Force-Controlled Robotic Ultrasound for Abdominal Volumetric Reconstruction Tactile In this paper, we present a robotic ultrasound acquisition system that integrates stereo vision, touch-based feedback, and expert-informed strategies to perform autonomous and adaptive abdominal scans. The system records freehand motion and force data from expert radiologists, creating a framework to capture transducer motion, applied forces, and anatomical scanning strategies. This expert data…	Oussama Khatib Team	ArXiv
2026-06-03	HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning Dexterous VLA Tactile Despite the importance of tactile sensing for reliable manipulation, most existing Vision-Language-Action (VLA) datasets remain vision-only, and those that do incorporate tactile information typically lack the joint combination of task diversity, language conditioning, and action trajectories. Furthermore, existing teleoperation pipelines rarely provide haptic feedback to the operator, despite…	Shan Luo Team	ArXiv
2026-06-03	SoftPINCH: EMG-Driven Soft Exoskeleton Assistance for Finger Flexion and Grasping Tactile Surface electromyography (sEMG) provides a non-invasive interface for detecting hand-movement intention and controlling wearable assistive devices. However, reliable EMG-driven hand assistance remains challenging because EMG signals are affected by noise, motion artifacts, electrode placement, muscle fatigue, and inter-subject variability. At the same time, many hand exoskeletons remain…	Saravana Prashanth Murali Babu Team	ArXiv
2026-06-03	TransTac: Visuo-Tactile Modality Transition via Ultraviolet-Encoded Transparent Elastomers Tactile Vision-based tactile sensors (VBTS) recover high-resolution contact geometry but typically rely on opaque elastomer layers that prevent visual transparency, while RGB-D cameras provide global depth perception yet degrade significantly at close range. To address this limitation, we present TransTac, a transparent ultraviolet (UV)-encoded binocular VBTS that integrates visual observation and…	Bin Fang Team	ArXiv
2026-06-02	Static and Dynamic Representations for Tactile Contact-Angle Estimation with Event-Based Sensors Dexterous Manipulation Tactile 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…	Benjamin Ward-Cherrier Team	ArXiv
2026-06-02	Toward Gripper-Integrated Active Electrosense for Pre-Contact Sensing in Underwater Soft Grippers Tactile Underwater manipulation often occurs under degraded visibility due to turbidity, glare, and gripper occlusion, limiting the reliability of vision-based perception during approach and grasping. In such settings, soft grippers are well suited for compliant interaction, but they typically lack an onboard pre-contact cue that can guide approach and closure when vision is unreliable. This extended…	Guangming Xie Team	ArXiv
2026-05-29	Learning Controlled Separation of Small Objects Between Two Fingers with a Tactile Skin Sim2Real Tactile We introduce and solve the novel task of controlled separation of small objects with two fingers of a multi-purpose robotic hand: after grasping into a box of small objects, the task is to drop as many of them until a desired number remains between the fingers. The objects are small compared to the width of the fingers but also in absolute terms. In our case little pellets with a diameter of only…	Berthold Bäuml Team	ArXiv
2026-05-29	SoFiE: Soft Finger Exoskeleton for Intelligent Grasping Manipulation Tactile 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…	Saravana Prashanth Murali Babu Team	ArXiv
2026-05-27	EIT-Pneumatic Hybrid Robotic Skin for Practical and Accurate Force Map Reconstruction Dexterous Tactile We present a hybrid robotic skin that combines electrical impedance tomography (EIT) with pneumatic tactile sensing to improve force reconstruction capability. The developed robotic skin is fabricated entirely by 3D printing and spray coating, making it affordable and easy to build. A Tikhonov-regularized inverse reconstruction, paired with per-pad pneumatic calibration, enables accurate…	Kyungseo Park Team	ArXiv
2026-05-27	Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language Dexterous Tactile Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for gentle manipulation due to scarce aligned vision-tactile-language data and the lack of effective closed-loop force feedback mechanisms. To address these challenges, we introduce Tabero, a benchmark and model suite for…	Renjing Xu Team	ArXiv / Web
2026-05-27	Tactile-Proprioceptive Sensor Fusion for Contact Wrench Estimation in Whole-Body Physical Human-Robot Interaction Tactile Direct physical guidance is a natural means of teaching and interacting with robots, and robotic skins make a key contribution by enabling sensitive contact sensing and localization. This paper presents a tactile-proprioceptive sensor fusion framework for natural physical human-robot interaction. Tactile cues from pneumatic skin pads serve as contact indicators that bypass the ambiguity between…	Kyungseo Park Team	ArXiv
2026-05-24	InvariantCloud: A Globally Invariant, Uniquely Indexed Point Cloud Framework for Robust 6-DoF Tactile Pose Tracking Tactile Recent advances in imitation learning and vision-language models highlight the need for high-fidelity tactile perception, with 6-DoF tactile object pose estimation providing a crucial foundation for precise robotic manipulation. We introduce InvariantCloud, a 6-DoF pose estimation framework that leverages the global invariance of surface marker constellations on vision-based tactile sensors. In…	Molong Duan Team	ArXiv
2026-05-23	IsaacIPC: Coupling High-Fidelity Simulation and Realistic Rendering for Contact-Rich Robotic Systems Dexterous Tactile We present IsaacIPC, a robotic simulation framework that couples GPU accelerated incremental potential contact (IPC) with IsaacSim/Lab. IsaacIPC maps simulated deformation between simulation and visual meshes, enabling real-time realistic rendering with applications to data collection and policy evaluation. For tactile sensing, we introduce the geometric mortar contact potential (GMCP), which…	Zhongqing Han Team	ArXiv
2026-05-22	TactileReflex: Noise-Statistics-Driven Vision-Tactile Reflex Control for Force-Sensitive Manipulation Dexterous Tactile Manipulating fragile deformable containers, such as disposable plastic cups filled with liquid, demands real-time grip-force adaptation within an extremely narrow force margin: insufficient force causes slip, while excessive force irreversibly deforms the thin wall. Existing approaches struggle to achieve such force-sensitive manipulation tasks. We propose a noise-statistics-based…	Qiang Nie Team	ArXiv
2026-05-21	TacO: Benchmarking Tactile Sensors for Object Manipulation Dexterous Tactile Vision-based learning from demonstrations has achieved remarkable success in enabling robots to perform manipulation tasks and high-level semantic reasoning, yet it remains insufficient for complex, contact-rich manipulation. While there is broad agreement that tactile sensing improves manipulation, there is no empirical guidance on which tactile sensors are best suited for which manipulation…	Xiaolong Wang Team	ArXiv
2026-05-20	roto 2.0: The Robot Tactile Olympiad Dexterous Tactile Tactile-based reinforcement learning (RL) is currently hindered by fragmented research and a focus on over-saturated orientation tasks. We introduce v2 of the Robot Tactile Olympiad (\texttt{roto 2.0}), a GPU-parallelised benchmark designed to standardise tactile-based RL across four distinct robotic morphologies (16-DOF to 24-DOF). Unlike prior benchmarks, roto focuses on end-to-end “blind”…	Sethu Vijayakumar Team	ArXiv
2026-05-20	Learning Robust Dexterous In-Hand Manipulation from Joint Sensors with Proprioceptive Transformer Dexterous Tactile In-hand object manipulation is a fundamental yet challenging capability for dexterous robots. Despite significant progress in dexterous manipulation, existing approaches rely heavily on vision or tactile sensing to track object states, while joint sensing – the most readily available modality on any robotic hand – remains largely overlooked, particularly for tendon-driven hands. In this paper,…	Robert K. Katzschmann Team	ArXiv
2026-05-19	VBT-MPC: Vision-Based Tactile MPC for Contour Following Dexterous Tactile Tactile sensing plays a key role in robotic manipulation, particularly in tasks like surface inspection. Successful execution requires maintaining contact while accurately tracking object contours. In this work, we propose a Vision-Based Tactile Model Predictive Control (VBT-MPC) framework for robotic contour following using a Vision-Based Tactile Sensor (VBTS) mounted in an eye-in-hand…	Pablo Gil Team	ArXiv
2026-05-18	TacSE3: Equivariant SE(3) Motion Estimation from Low-Texture Visuotactile Images for In-Gripper Tracking and Compensation Dexterous Tactile Robotic in-hand manipulation requires reliable object-motion tracking under frequent visual occlusion, yet low-texture visuotactile images provide few stable correspondences for conventional image- or geometry-matching methods. This paper presents TacSE3, a tactile motion-estimation pipeline that converts low-texture visuotactile observations into a decoupled three-dimensional force field and…	Michael Yu Wang Team	ArXiv
2026-05-18	Mixtac: A Novel Bio-Inspired Hybrid Tactile Sensor with Synergistic Event-Frame Perception Tactile Vision based and event based tactile sensors are important in robotic manipulation research. However, they suffer from a fundamental tradeoff: vision based sensors have low sampling rates, while event based sensors are prone to drift during long term static force estimation. To solve this challenge and achieve human level tactile perception, the novel hybrid event frame tactile sensor (Mixtac) is…	Bin He Team	ArXiv
2026-05-18	WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform Tactile World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about environmental dynamics. However, existing embodied world model benchmarks are still largely confined to vision-only prediction, offline embodied applications, and simulator-based evaluation, making them insufficient for assessing increasingly…	Yong Li Team	ArXiv
2026-05-17	Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms Tactile Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and interaction dynamics that remote sensors cannot replace. However, unimodal tactile perception is inherently limited by its sparse spatial coverage and lack of global semantic context. With the recent explosion in deep learning and large language…	Hui Xiong Team	ArXiv
2026-05-13	TouchAnything: A Dataset and Framework for Bimanual Tactile Estimation from Egocentric Video Dexterous Tactile Egocentric human video data, which captures rich human-environment interactions and can be collected at scale, has become a key driver of embodied intelligence research. However, existing egocentric datasets typically lack tactile sensing, a critical modality that provides direct cues about contact, force, and pressure in human-object interaction. Without such signals, models struggle to learn…	Shuo Yang Team	ArXiv
2026-05-13	Phantom Force: Injecting Adversarial Tactile Perceptions into Embodied Intelligence via EMI Tactile Embodied intelligent robots rely on tactile sensors to interact with the physical world safely. While the security of visual perception systems has been studied (e.g., adversarial samples), the integrity of the tactile sensory channel remains unexplored. This work explores a vulnerability in Hall-effect fingertip sensors, showing their susceptibility to intentional Electromagnetic Interference…	Sze Yiu Chau Team	ArXiv
2026-05-12	Mapping Embodied Affective Touch Strategies on a Humanoid Robot Tactile Affective touch in human-robot interaction is shaped not only by emotional intent, but also by robot embodiment, including touch location, physical constraints, and perceived agency or social role. Existing HRI studies typically focus on one or two isolated body parts, limiting understanding of how affective touch generalises across the full humanoid body. We present a study with 32 participants…	Tony Belpaeme Team	ArXiv
2026-05-07	TouchDrive: Electronics-Free Tactile Sensing Interface for Assistive Grasping Dexterous Tactile Assistive robotic grasping plays an important role in enabling safe and adaptive manipulation of diverse objects. However, existing systems often rely on electronic sensing and multi-stage processing pipelines, increasing system complexity and reducing accessibility. To address these limitations, we present TouchDrive, a cost-effective, electronics-free tactile sensing interface for assistive…	Klas Hjort Team	ArXiv
2026-05-06	Active Contact Sensing for Robust Robot-to-Human Object Handover Tactile Robot-to-human object handover is an essential skill for robot assistants, from serving drinks at home to passing surgical tools in the operating room. We expect robots to perform handover robustly – to release the object only after a firm human grasp while ignoring incidental touches. Existing passive-sensing methods struggle to generalize across diverse objects and human behaviors, as they…	David Hsu Team	ArXiv
2026-05-04	Vision-Based Structural Damage Identification in Vibrating Beams via Dynamic Mode Decomposition Tactile Structural damage detection using non-contact sensing remains a challenging problem in structural health monitoring. This study presents a data-driven framework based on Dynamic Mode Decomposition (DMD) for extracting structural dynamics directly from high-speed video recordings of vibrating structures. Within this approach, the underlying dynamics are approximated by a linear operator, whose…	Shabbir Ahmed Team	ArXiv
2026-05-02	High-Speed, Scalable Sensor Readout for Dexterous Robotic Hands via Shift-Register Multiplexing Dexterous Tactile Dexterous robotic hands require high-speed multimodal sensing across many degrees of freedom, yet existing readout architectures often impose trade-offs between sensor count, wiring complexity, and sampling bandwidth. This paper presents a scalable analog sensor readout architecture based on a serial-in parallel-out (SIPO) shift-register principle. The proposed architecture supports versatile…	Robert K. Katzschmann Team	ArXiv
2026-04-30	FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems Dexterous Tactile 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…	Yunzhu Li Team	ArXiv / Web
2026-04-30	DOT-Sim: Differentiable Optical Tactile Simulation with Precise Real-to-Sim Physical Calibration Tactile Simulating optical tactile sensors presents significant challenges due to their high deformability and intricate optical properties. To address these issues and enable a physically accurate simulation, we propose DOT-Sim: Differentiable Optical Tactile Simulation. Unlike prior simulators that rely on simplified models of deformable sensors, DOT-Sim accurately captures the physical behavior of…	Leonidas Guibas Team	ArXiv
2026-04-29	Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies Dexterous Tactile LearnedControl Quadrupedal loco-manipulation is commonly built on visual perception and proprioception. Yet reliable contact-rich manipulation remains difficult: vision and proprioception alone cannot resolve uncertain, evolving interactions with the environment. Tactile sensing offers direct contact observability, but scalable tactile-aware learning framework for quadrupedal loco-manipulation is still…	Yu She Team	ArXiv
2026-04-28	Improving Sensing Coverage and Compliance of 3D-Printed Artificial Skins Through Multi-Modal Sensing and Soft Materials Dexterous Tactile 3D-printed artificial skins are a scalable approach to whole-body tactile and proximity coverage, but prior implementations have been limited to unimodal sensing and rigid materials. To improve the practical usability of 3D-printed artificial skins, we present a hybrid time-of-flight (ToF) and self-capacitance (SC) sensing skin that demonstrates multi-modal sensing integration, soft compliant…	Alessandro Roncone Team	ArXiv
2026-04-27	SPLIT: Separating Physical-Contact via Latent Arithmetic in Image-Based Tactile Sensors Dexterous Tactile Training machine learning models for robotic tactile sensing requires vast amounts of data, yet obtaining realistic interaction data remains a challenge due to physical complexity and variability. Simulating tactile sensors is thus a crucial step in accelerating progress. This paper presents SPLIT, a novel method for simulating image-based tactile sensors, with a primary focus on the DIGIT…	Nicolás Navarro-Guerrero Team	ArXiv
2026-04-27	An analysis of sensor selection for fruit picking with suction-based grippers Tactile Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision-based perception and multi-sensor learning approaches for pick state estimation. However,…	Joseph R. Davidson Team	ArXiv
2026-04-22	FingerEye: Continuous and Unified Vision-Tactile Sensing for Dexterous Manipulation Dexterous Tactile Dexterous robotic manipulation requires comprehensive perception across all phases of interaction: pre-contact, contact initiation, and post-contact. Such continuous feedback allows a robot to adapt its actions throughout interaction. However, many existing tactile sensors, such as GelSight and its variants, only provide feedback after contact is established, limiting a robot’s ability to…	Lin Shao Team	ArXiv
2026-04-22	VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation Dexterous Tactile Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging. This is largely due to the lack of datasets with rich physical interaction signals, systematic task organization, and sufficient scale. To address these limitations, we introduce the VTOUCH dataset. It leverages vision based tactile sensing to provide…	Yufei Liu Team	ArXiv
2026-04-22	ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation Dexterous Tactile Tactile sensors are increasingly integrated into dexterous robotic manipulators to enhance contact perception. However, learning manipulation policies that rely on tactile sensing remains challenging, primarily due to the trade-off between fidelity and computational cost of soft-body simulations. To address this, we present ETac, a tactile simulation framework that models elastomeric soft-body…	Chenxi Xiao Team	ArXiv
2026-04-20	SpaceDex: Generalizable Dexterous Grasping in Tiered Workspaces Dexterous Tactile Generalizable grasping with high-degree-of-freedom (DoF) dexterous hands remains challenging in tiered workspaces, where occlusion, narrow clearances, and height-dependent constraints are substantially stronger than in open tabletop scenes. Most existing methods are evaluated in relatively unoccluded settings and typically do not explicitly model the distinct control requirements of arm…	Ning Tan Team	ArXiv
2026-04-19	MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation Dexterous Tactile High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand space, increase end-effector mass, and suffer from heat accumulation near the hand. Remote tendon-driven actuation offers an alternative by relocating motors to the robot base or an external motor hub, thereby freeing the fingers and palm for…	Ping Luo Team	ArXiv
2026-04-14	Learning Versatile Humanoid Manipulation with Touch Dreaming Dexterous LearnedControl Tactile Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, dexterous hands, and contact-aware perception under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based whole-body controller that provides stable lower-body and…	Ding Zhao Team	ArXiv
2026-04-14	FastGrasp: Learning-based Whole-body Control method for Fast Dexterous Grasping with Mobile Manipulators Dexterous Tactile LearnedControl Fast grasping is critical for mobile robots in logistics, manufacturing, and service applications. Existing methods face fundamental challenges in impact stabilization under high-speed motion, real-time whole-body coordination, and generalization across diverse objects and scenarios, limited by fixed bases, simple grippers, or slow tactile response capabilities. We propose \textbf{FastGrasp}, a…	Yuexin Ma Team	ArXiv
2026-04-12	OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction Dexterous Tactile UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physical interaction signals. This becomes a fundamental limitation in contact-rich manipulation, where success depends on contact dynamics such as tactile interaction, internal grasping force, and external…	Zhongyuan Wang Team	ArXiv
2026-04-12	i-Tac: Inverse Design of 3D-Printed Tactile Elastomers with Scalable and Tunable Optical and Mechanical Properties Tactile Elastomers are central to vision-based tactile sensors (VBTSs), where they transduce external contact into observable deformation. Different VBTS architectures, however, require distinct optical and mechanical properties, particularly transparency and hardness. Conventional elastomer design relies on a forward, trial-and-error optimisation process from material preparation to property evaluation,…	Dandan Zhang Team	ArXiv
2026-04-10	TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches Dexterous Tactile Accurate object geometry estimation is essential for many downstream tasks, including robotic manipulation and physical interaction. Although vision is the dominant modality for shape perception, it becomes unreliable under occlusions or challenging lighting conditions. In such scenarios, tactile sensing provides direct geometric information through physical contact. However, reconstructing…	Wenzhen Yuan Team	ArXiv / Web
2026-04-09	A-SLIP: Acoustic Sensing for Continuous In-hand Slip Estimation Tactile Reliable in-hand manipulation requires accurate real-time estimation of slip between a gripper and a grasped object. Existing tactile sensing approaches based on vision, capacitance, or force-torque measurements face fundamental trade-offs in form factor, durability, and their ability to jointly estimate slip direction and magnitude. We present A-SLIP, a multi-channel acoustic sensing system…	Jeffrey Ichnowski Team	ArXiv
2026-04-02	Cross-Modal Visuo-Tactile Object Perception Tactile Estimating physical properties is critical for safe and efficient autonomous robotic manipulation, particularly during contact-rich interactions. In such settings, vision and tactile sensing provide complementary information about object geometry, pose, inertia, stiffness, and contact dynamics, such as stick-slip behavior. However, these properties are only indirectly observable and cannot always…	Mohsen Kaboli Team	ArXiv
2026-04-01	How to Train your Tactile Model: Tactile Perception with Multi-fingered Robot Hands Tactile Rapid deployment of new tactile sensors is essential for scalable robotic manipulation, especially in multi-fingered hands equipped with vision-based tactile sensors. However, current methods for inferring contact properties rely heavily on convolutional neural networks (CNNs), which, while effective on known sensors, require large, sensor-specific datasets. Furthermore, they require retraining…	Efi Psomopoulou Team	ArXiv
2026-03-30	Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment Tactile Sim2Real Visuotactile sensors are indispensable for contact-rich robotic manipulation tasks. However, policy learning with tactile feedback in simulation, especially for online reinforcement learning (RL), remains a critical challenge, as it demands a delicate balance between physics fidelity and computational efficiency. To address this challenge, we present Tac2Real, a lightweight visuotactile…	Jiangmiao Pang Team	ArXiv
2026-03-26	When Sensing Varies with Contexts: Context-as-Transform for Tactile Few-Shot Class-Incremental Learning Tactile Few-Shot Class-Incremental Learning (FSCIL) can be particularly susceptible to acquisition contexts with only a few labeled samples. A typical scenario is tactile sensing, where the acquisition context ({\it e.g.}, diverse devices, contact state, and interaction settings) degrades performance due to a lack of standardization. In this paper, we propose Context-as-Transform FSCIL (CaT-FSCIL) to…	Zheng-Jun Zha Team	ArXiv
2026-03-26	Shared Representation for 3D Pose Estimation, Action Classification, and Progress Prediction from Tactile Signals Tactile Estimating human pose, classifying actions, and predicting movement progress are essential for human-robot interaction. While vision-based methods suffer from occlusion and privacy concerns in realistic environments, tactile sensing avoids these issues. However, prior tactile-based approaches handle each task separately, leading to suboptimal performance. In this study, we propose a Shared…	Kyung-Joong Kim Team	ArXiv
2026-03-22	Geometrically Plausible Object Pose Refinement using Differentiable Simulation Tactile State-of-the-art object pose estimation methods are prone to generating geometrically infeasible pose hypotheses. This problem is prevalent in dexterous manipulation, where estimated poses often intersect with the robotic hand or are not lying on a support surface. We propose a multi-modal pose refinement approach that combines differentiable physics simulation, differentiable rendering and…	Akansel Cosgun Team	ArXiv
2026-03-22	Bayesian Active Object Recognition and 6D Pose Estimation from Multimodal Contact Sensing Tactile We present an active tactile exploration framework for joint object recognition and 6D pose estimation. The proposed method integrates wrist force/torque sensing, GelSight tactile sensing, and free-space constraints within a Bayesian inference framework that maintains a belief over object class and pose during active tactile exploration. By combining contact and non-contact evidence, the…	Raymond H. Cuijpers Team	ArXiv
2026-03-20	Zero Shot Deformation Reconstruction for Soft Robots Using a Flexible Sensor Array and Cage Based 3D Gaussian Modeling Tactile We present a zero-shot deformation reconstruction framework for soft robots that operates without any visual supervision at inference time. In this work, zero-shot deformation reconstruction is defined as the ability to infer object-wide deformations on previously unseen soft robots without collecting object-specific deformation data or performing any retraining during deployment. Our method…	Tingyu Cheng Team	ArXiv
2026-03-19	Contact Status Recognition and Slip Detection with a Bio-inspired Tactile Hand Tactile Stable and reliable grasp is critical to robotic manipulations especially for fragile and glazed objects, where the grasp force requires precise control as too large force possibly damages the objects while small force leads to slip and fall-off. Although it is assumed the objects to manipulate is grasped firmly in advance, slip detection and timely prevention are necessary for a robot in…	Longhui Qin Team	ArXiv
2026-03-19	ViTac-Tracing: Visual-Tactile Imitation Learning of Deformable Object Tracing Tactile Sim2Real Deformable objects often appear in unstructured configurations. Tracing deformable objects helps bringing them into extended states and facilitating the downstream manipulation tasks. Due to the requirements for object-specific modeling or sim-to-real transfer, existing tracing methods either lack generalizability across different categories of deformable objects or struggle to complete tasks…	Shan Luo Team	ArXiv
2026-03-18	DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation Tactile Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation. While human-centric data collection systems lower the barrier to scaling, they struggle to capture the tactile information during physical interactions. Motivated by this, we present DexViTac, a portable, human-centric data collection system tailored for contact-rich…	Xiaotian Ding Team	ArXiv
2026-03-16	HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing Tactile Tactile sensing is a crucial capability for Vision-Language-Action (VLA) architectures, as it enables dexterous and safe manipulation in contact-rich tasks. However, reliance on dedicated tactile hardware increases cost and reduces reproducibility across robotic platforms. We argue that tactile-aware manipulation can be learned offline and deployed without direct haptic feedback at inference. To…	Dzmitry Tsetserukou Team	ArXiv
2026-03-14	GelSphere: An Omnidirectional Rolling Vision-Based Tactile Sensor for Online 3D Reconstruction and Normal Force Estimation Tactile We present GelSphere, a spherical vision-based tactile sensor designed for real-time continuous surface scanning. Unlike traditional vision-based tactile sensors that can only sense locally and are damaged when slid across surfaces, and cylindrical tactile sensors that can only roll along a fixed direction, our design enables omnidirectional rolling on surfaces. We accomplish this through our…	Wenzhen Yuan Team	ArXiv
2026-03-11	FG-CLTP: Fine-Grained Contrastive Language Tactile Pretraining for Robotic Manipulation Tactile Recent advancements in integrating tactile sensing into vision-language-action (VLA) models have demonstrated transformative potential for robotic perception. However, existing tactile representations predominantly rely on qualitative descriptors (e.g., texture), neglecting quantitative contact states such as force magnitude, contact geometry, and principal axis orientation, which are…	Shuo Wang Team	ArXiv
2026-03-11	Learning Bimanual Cloth Manipulation with Vision-based Tactile Sensing via Single Robotic Arm Tactile Robotic cloth manipulation remains challenging due to the high-dimensional state space of fabrics, their deformable nature, and frequent occlusions that limit vision-based sensing. Although dual-arm systems can mitigate some of these issues, they increase hardware and control complexity. This paper presents Touch G.O.G., a compact vision-based tactile gripper and perception/control framework for…	Petar Kormushev Team	ArXiv
2026-03-11	TacLoc: Global Tactile Localization on Objects from a Registration Perspective Tactile Pose estimation is essential for robotic manipulation, particularly when visual perception is occluded during gripper-object interactions. Existing tactile-based methods generally rely on tactile simulation or pre-trained models, which limits their generalizability and efficiency. In this study, we propose TacLoc, a novel tactile localization framework that formulates the problem as a one-shot…	Huan Yin Team	ArXiv
2026-03-10	MuxGel: Simultaneous Dual-Modal Visuo-Tactile Sensing via Spatially Multiplexing and Deep Reconstruction Tactile High-fidelity visuo-tactile sensing is important for precise robotic manipulation. However, most vision-based tactile sensors face a fundamental trade-off: opaque coatings enable tactile sensing but block pre-contact vision. To address this, we propose MuxGel, a spatially multiplexed sensor that captures both external visual information and contact-induced tactile signals through a single camera….	Yu She Team	ArXiv
2026-03-10	NLiPsCalib: An Efficient Calibration Framework for High-Fidelity 3D Reconstruction of Curved Visuotactile Sensors Tactile Recent advances in visuotactile sensors increasingly employ biomimetic curved surfaces to enhance sensorimotor capabilities. Although such curved visuotactile sensors enable more conformal object contact, their perceptual quality is often degraded by non-uniform illumination, which reduces reconstruction accuracy and typically necessitates calibration. Existing calibration methods commonly rely…	Chenxi Xiao Team	ArXiv
2026-03-09	CONTACT: CONtact-aware TACTile Learning for Robotic Disassembly Tactile Robotic disassembly involves contact-rich interactions in which successful manipulation depends not only on geometric alignment but also on force-dependent state transitions. While vision-based policies perform well in structured settings, their reliability often degrades in tight-tolerance, contact-dominated, or deformable scenarios. In this work, we systematically investigate the role of…	Yu She Team	ArXiv
2026-03-09	Tactile Recognition of Both Shapes and Materials with Automatic Feature Optimization-Enabled Meta Learning Tactile Tactile perception is indispensable for robots to implement various manipulations dexterously, especially in contact-rich scenarios. However, alongside the development of deep learning techniques, it meanwhile suffers from training data scarcity and a time-consuming learning process in practical applications since the collection of a large amount of tactile data is costly and sometimes even…	Longhui Qin Team	ArXiv
2026-03-09	FlowTouch: View-Invariant Visuo-Tactile Prediction Tactile Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when an object is touched. This precludes their use during planning and the initial execution phase of a…	Wolfram Burgard Team	ArXiv