Tactile Sensing for Robotic Manipulation

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Overview

This field studies how robots can use touch—such as pressure maps, shear, vibration, and contact geometry—to perceive interactions that cameras may miss. Researchers build optical, resistive, capacitive, and acoustic sensors, then use tactile feedback for tasks such as stable grasping, slip correction, delicate force control, and peg insertion. Learning approaches range from fast reactive controllers and tactile state estimators to vision-tactile policies, world models, and simulation-to-real training; benchmarks increasingly test different sensors, robot hands, and contact-rich tasks. A central challenge is making tactile representations and policies transfer across sensors and settings, since the value of touch depends on the task and simply adding tactile input does not guarantee better control.

Latest papers

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178 papers33 papers in the last four weeks, down 6% on the four weeks before. 0.2% of all new papers.

Jun 8, 2026

TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation

Vision-Language-Action (VLA) models have become a powerful framework for robotic manipulation, and recent studies have introduced tactile or force feedback into VLAs to address contact-rich tasks. However, these models are typically deployed as offline policies. When contact conditions shift from the training distribution, the policy cannot perform online adaptation, leading to problems such as inappropriate contact forces and inefficient retries. Therefore, we propose TORL-VLA, a tactile-guided online reinforcement learning framework that couples tactile feedback with policy refinement for contact-rich manipulation. Our method introduces a tactile-derived wrench-aware VLA to predict reference actions and future wrench sequences, while a lightweight online RL module is used to refine the reference actions. To stabilize learning from mixed exploratory policy-generated and human-intervention data, we introduce an intervention-censored critic that prevents post-intervention success from being wrongly credited to policy-generated actions preceding intervention. Real-robot experiments on long-horizon contact-rich tasks, including latch manipulation, coffee-cup placement, and egg handling, show that TORL-VLA improves success rates at both subtask and full-task levels, as well as time-bounded execution efficiency over strong baselines. Project page: https://torl-vla.github.io/

Huaihang Zheng, Yi Yang, Kai Ma +8

Meituan · State Key Lab of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS · Beijing Institute of Technology +2

RL for RoboticsLong-Horizon Robotic ManipulationRobot Skill LearningContact-Rich Robotic ManipulationTactile Sensing for Robotic ManipulationVLMs for Robotics
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Jun 8, 2026

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 SensingVideo Diffusion ModelsTactile SensingTactile Sensing for Robotic Manipulation
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Jun 7, 2026

RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation

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 demonstrations, without leveraging geometric priors. As a result, they are often data-inefficient and generalize poorly when visual observations are degraded. To address this limitation, we propose a framework that explicitly grounds physical contacts in the image domain. Using robot forward kinematics and camera calibration, we project tactile sensor locations directly onto the RGB image plane. We then render force-modulated Gaussian saliency maps to model spatial uncertainty arising from kinematic and calibration errors. By integrating these 2D spatial anchors through a zero-initialized conditioning architecture, our method injects physical contact priors into standard visual backbones while preserving pre-trained visual representations. We evaluate our method on six dexterous manipulation tasks in both simulation and the real world under severe visual occlusions. Real-world experiments show that explicit RGB-S grounding in the image domain improves real-world occluded manipulation success rates by 26.726.7 percentage points over the strongest implicit visuo-tactile baseline, suggesting its improved spatial reasoning and robustness to occlusion. Project page: touch-as-saliency.github.io

Shengcheng Luo, Kefei Wu, Xiaoying Zhou +3

ShanghaiTech University · Beijing Institute for General Artificial Intelligence

Contact-Rich Robotic ManipulationVisuo-Tactile Representation LearningRobotic ManipulationTactile Sensing for Robotic ManipulationMultimodal Sensor FusionVisual Grounding for Robotics
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Jun 7, 2026

Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation

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 visual observations, and tactile dynamics. Specifically, Dream-Tac introduces (i) contact-gated visuotactile fusion to selectively integrate tactile signals and (ii) a contact-aware attention bias to better regulate cross-modal interactions during manipulation. To support real-time deployment, we further design a dual-level acceleration strategy, reformulating the contact-aware bias to preserve the fused attention path during training and introducing cache-based diffusion acceleration at inference, achieving up to 2.9×\times faster training and 1.8×\times faster inference. Across six contact-rich manipulation tasks, Dream-Tac improves action accuracy by 31.7% on average, demonstrating the effectiveness of unified visuotactile world modeling.Code is available at https://github.com/LYFCLOUDFAN/Dream-Tac.

Yunfan Lou, Yifan Ye, Yankai Fu +7

Peking University Beijing, China · Nanjing University Nanjing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University Beijing, China

World Models for RoboticsContact-Rich Robotic ManipulationAction-Conditioned World ModelsWorld Action ModelsTactile Sensing for Robotic ManipulationMultimodal Sensor Fusion+1
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Jun 4, 2026

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 an effective architecture using modality-specific convolutional stems and transformer-based fusion that effectively integrates information from an RGB camera stream, a vision-based GelSight Mini sensor and a high-frequency event-based Evetac sensor. This multisensory representation then conditions a flow-matching policy for solving downstream tasks. Our systematic study across five contact-rich manipulation tasks demonstrates the effectiveness of multi-resolution tactile features in imitation learning wherein MiTaS achieves an average success rate of 80 %, while vision-only (35.5,%) and visuotactile (55.5,%, 63,%) baselines cannot solve the task reliably. 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 SensingContact-Rich Robotic ManipulationVisuo-Tactile Representation LearningRobot Imitation LearningTactile Sensing for Robotic ManipulationMultimodal Sensor Fusion
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May 29, 2026

Learning Controlled Separation of Small Objects Between Two Fingers with a Tactile Skin

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 6mm are handled. We show that the task can be performed purely tactile (no vision) using a spatially-resolved tactile skin on a fingertip. The separation policy is trained in simulation via reinforcement learning using a straightforward sparse reward, which basically checks if the desired number of objects is reached. In simulation experiments, we provide an exhaustive analysis of the benefits of using spatially-resolved tactile feedback: while an ideal (high-resolution) tactile sensor allows solving the task almost perfectly, a sensor with lower spatial resolution (here 4x4 taxels) still leads to an improvement of up to 20% compared to using only the fingers' joint sensors. For this analysis, we further train an estimator alongside the policy that predicts the ground truth contact positions. Finally, we demonstrate the successful sim-to-real transfer for the DLR-Hand II equipped with a tactile skin.

Ulf Kasolowsky, Berthold Bäuml

Learning AI for Dextrous Robots Lab (aidx-lab.org), Technical University of Munich, Germany · DLR Institute of Robotics and Mechatronics, German Aerospace Center

Tactile Sensing for Robotic Manipulation
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May 28, 2026

ARISTO Hand: Sensing-Driven Distal Hyperextension for Fine-Grained Manipulation

Manipulating thin objects requires precise contact geometry and reliable force perception, yet many anthropomorphic robotic hands lack the mechanical and sensing capabilities needed for such interactions. We present the ARISTO Hand, a tendon-driven robotic hand that integrates active distal hyperextension with a hybrid fingertip-sensing architecture that combines a rigid, nail-mounted force-torque sensor and a soft capacitive tactile array. Active hyperextension enables controlled fingertip engagement beyond the kinematic limits of standard flexion, increasing pull-out force by 2.76x for object thicknesses of 1-20 mm while preserving the nominal grasp capability. The rigid nail-mounted sensor provides reliable force measurements during edge contacts, where the sensitivity of proprioceptive force estimation degrades as the contact geometry approaches kinematic singularities. We validate the proposed architecture through quantitative force characterization and a multi-stage SD card extraction and insertion task. Video and supplementary materials are available at: https://aristohand.github.io

Aaron Kim, Dong Ho Kang, Mark Helwig +4

Human Centered Robotics Lab at The University of Texas at Austin, TX, USA. · Sony Group Corporation at Tokyo, Japan.

Robotic Hand DesignRobotic GraspingDexterous ManipulationRoboticsTactile Sensing for Robotic Manipulation
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May 27, 2026

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 RoboticsTactile SensingContact-Rich Robotic ManipulationSim-to-Real TransferRobotic ManipulationTactile Sensing for Robotic Manipulation
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May 27, 2026

Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language

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 gentle, language-conditioned robotic manipulation that demands fine-grained contact force perception. First, the Tabero benchmark addresses the scarcity of tactile data by presenting a data-efficient pipeline that repurposes open-source robot manipulation trajectories to generate diverse vision-tactile-language tasks, and establishes a multidimensional evaluation protocol that measures task success alongside physical interaction quality. Second, we propose Tabero-VTLA, an architecture with a decoupled force-position command interface; the resulting force-position commands are executed by a fixed hybrid controller to enable real-time, force-aware manipulation. Evaluated on Tabero, our model maintains high task success while reducing average grip force by over 70% under gentle instructions, demonstrating its ability to modulate interaction forces based on multimodal experience. Our code is publicly available at https://github.com/NathanWu7/Tabero.

Qiwei Wu, Rui Zhang, Xin Xiang +4

Nvidia, Beijing, China · Hong Kong University of Sci

Language-Conditioned Robot ManipulationRobotic ManipulationForce-Controlled Robotic ManipulationRobot Manipulation BenchmarksTactile Sensing for Robotic ManipulationVLMs for Robotics
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May 24, 2026

InvariantCloud: A Globally Invariant, Uniquely Indexed Point Cloud Framework for Robust 6-DoF Tactile Pose Tracking

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 contrast to recent approaches, our one-shot globally invariant point cloud registration suppresses cumulative drift and overcomes long-standing limitations in accurately estimating yaw (Z-axis) rotation. Experimental verifications show that InvariantCloud achieves superior yaw tracking accuracy and re-localization repeatability compared to existing benchmarks, demonstrating its precision and robustness in long-sequence manipulation tasks.

Pengfei Ye, Yuxiang Ma, Yi Zhou +3

Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology · Department of Mechanical Engineering, Massachusetts Institute of Technology · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong

Vision-Based Tactile SensingObject Pose EstimationPoint Cloud RegistrationTactile Sensing for Robotic Manipulation
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May 22, 2026

TactileReflex: Noise-Statistics-Driven Vision-Tactile Reflex Control for Force-Sensitive Manipulation

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 calibration-driven reflex control paradigm with vision-based tactile sensing: by analyzing the sensor's intrinsic noise characteristics (via a brief static-hold-and-unload protocol), we directly derive all controller thresholds, eliminating external force calibration, trial-and-error manual tuning, or material-specific physical models. Instantiating this paradigm, we present TactileReflex, a three-channel closed-loop controller that extracts three image-level proxies, shear intensity (SyS_y), contact intensity (FnF_n), and center of pressure (CC), from dual visuo-tactile sensors and drives prioritized reflex channels at ~12 Hz for slip suppression, weight-adaptive release, and force protection. Each channel closes the loop directly on its proxy via noise-derived thresholds. Ablation demonstrates that only the full three-channel system is able to prevent irreversible container deformation (5/5 success vs. at most 1/5 for partial configurations). In a dynamic pouring task, fixed-effort baselines fail in all 10 attempts due to pose drift, while TactileReflex achieves 9/10 success across two water volumes. As a self-contained and interpretable controller, TactileReflex can serve as a plug-and-play safety layer beneath high-level manipulation pipelines, including haptic-free VR teleoperation and vision-language-action (VLA) policies.

Ziyan Feng, Yulong Fu, Zheng Li +6

Thrust of Robotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Robotic ControlVision-Based Tactile SensingRobotic GraspingTactile SensingDeformable Object ManipulationRobotic Manipulation+2
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May 21, 2026

TacO: Benchmarking Tactile Sensors for Object Manipulation

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 tasks. In this paper, we provide a systematic, task-driven evaluation of tactile sensors for robot manipulation and propose a framework for selecting and evaluating sensors based on manipulation policy performance. Separate manipulation policies are trained for tactile sensors of four distinct modalities: visual, acoustic, magnetic, and resistive, across three tasks: pick-and-place with unknown mass, object reorientation, and plug insertion. For each task, an analysis of how sensor properties such as spatial resolution, shear sensing, and tactile representation, and the inherent material friction affect task performances is done. Rather than tactile sensing being universally beneficial in the same way, our results show that the usefulness of tactile information depends strongly on sensor modality, material properties, and the specific manipulation tasks. All of the tactile sensors, code, data, and hardware setup will be publicly available on the project website.

Anya Zorin, Zilin Si, Myungsun Park +11

UC San Diego United States · CMU · UC San Diego +2

Tactile SensingContact-Rich Robotic ManipulationRobot Manipulation BenchmarksTactile Sensing for Robotic Manipulation
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May 20, 2026

roto 2.0: The Robot Tactile Olympiad

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 (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" manipulation, utilising only proprioception and tactile sensing without state information or distillation. We demonstrate a significant performance leap, with our blind agents achieving 13 Baoding ball rotations in 10 seconds, an order of magnitude faster than current state-of-the-art speeds. By open-sourcing our environments and robustly tuned baselines, we reduce the barrier to entry and enable researchers to prioritise fundamental algorithmic challenges over tedious RL tuning. Website: https://elle-miller.github.io/roto/

Elle Miller, Jayaram Reddy, Ayush Deshmukh +4

University of Edinburgh, UK · National University Singapore, Singapore

RL for RoboticsDexterous ManipulationRobotic ManipulationDynamic Robotic ManipulationRobot Manipulation BenchmarksTactile Sensing for Robotic Manipulation
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May 19, 2026

VBT-MPC: Vision-Based Tactile MPC for Contour Following

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 configuration. The proposed controller operates directly in contour features space, thereby avoiding the need for separate pose-estimation modules or complex force-control architectures. We further compare our VBT-MPC with visual-servoing strategies adapted to tactile features, and evaluate contour tracking on objects with diverse geometries and materials in both simulation and real-world experiments.

Edison Velasco-Sanchez, Luis F. Recalde, Guanrui Li +1

AUROVA Lab, Computer Science Research Institute, University of Alicante, 03690 Alicante, Spain · Worcester Polytechnic Institute, Robotics Engineering, Worcester, MA 01609, USA

Vision-Based Tactile SensingTactile SensingModel Predictive ControlTactile Sensing for Robotic Manipulation
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May 18, 2026

TacSE3: Equivariant SE(3) Motion Estimation from Low-Texture Visuotactile Images for In-Gripper Tracking and Compensation

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 estimates incremental rigid-body motion on SE(3). The method derives planar translation from contact-centroid motion and estimates rotation primarily from shear-related tactile responses, yielding a physically interpretable signal for in-gripper tracking and compensation. Experiments with paired DM-Tac fingertip sensors show that dual-sensor sensing reduces translation-rotation ambiguity, supports rotation tracking across axes and object geometries, and provides a lightweight compensation signal that improves disturbance tolerance in downstream manipulation tasks without retraining the base policy.

Zhongyuan Liao, Junzhe Wang, Qingyang Liu +6

Hong Kong Center for Construction Robotics, The Hong Kong University of Science and Technology, Hong Kong, China · The Hong Kong University of Science and Technology (Guangzhou), China · School of Engineering, Great Bay University, Dongguan, China

Tactile SensingObject Pose EstimationRobotic ManipulationTactile Sensing for Robotic Manipulation
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May 17, 2026

Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms

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 models, integrating tactile with vision and language has become essential to bridge physical interaction with semantic reasoning, leading to the emergence of Multimodal Tactile Fusion. Despite rapid progress, the existing researches remain fragmented across disparate datasets, sensing modalities, and tasks, lacking a unified theoretical framework. To address this gap, this paper provides a comprehensive survey of multimodal tactile fusion research up to the first quarter of 2026. We propose a hierarchical taxonomy that organizes the field into two primary dimensions: multimodal datasets and multimodal methods. On the data side, we categorize resources ranging from Tactile-Vision datasets, Tactile-Language datasets, Tactile-Vision-Language datasets, and Tactile-Vision-Other datasets. On the method side, we structure prior work into three core pillars: (1) Multimodal Perception and Recognition, which focuses on object understanding and grasp prediction; (2) Cross-Modal Generation, focusing on bidirectional translation between tactile, vision, and text; and (3) Multimodal Interaction, emphasizing feedback control and language-guided manipulation. Furthermore, we summarize representative tactile sensing hardware, review commonly used evaluation metrics and benchmark settings, and discuss current challenges and promising future directions.

Zhixiang Cao, Di Tian, Runwei Guan +11

School of Electronic Science and Engineering, Xi’an Jiaotong University, China · Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), China · State Key Laboratory for Novel Software Technology, Nanjing University, China +7

Tactile SensingMultimodal GenerationMultimodal FusionTactile Sensing for Robotic ManipulationMultimodal Sensor Fusion
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May 9, 2026

A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning

We present a visuo-tactile data-collection system that generates temporally structured, contact-rich demonstrations for imitation learning. Conventional systems often decouple the operator from contact forces, which hinders the demonstration of subtle force modulation. Our system introduces a direct-drive gripper that the operator actuates with the fingers, preserving natural haptic feedback. Integrated visual sensors and custom tactile arrays capture image streams and contact geometry. A handle-mounted push button enables the operator to annotate the task's temporal structure in real time by marking task-critical regions. By fusing in-hand force perception with in-situ temporal annotation, the system produces multimodal datasets designed for coarse-to-fine learning algorithms that exploit structural task knowledge, enabling the development of high-quality manipulation policies.

Yeseung Kim, Nayoung Oh, Jun Park +2

Korea Advanced Institute of Science and Technology, Yuseong-gu Daehak-ro 291, Daejeon 34141, South Korea

Haptic FeedbackImitation LearningTactile Sensing for Robotic Manipulation
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May 8, 2026

AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models

Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face challenges in contact-rich manipulation scenarios that require precise physical interactions. To address this limitation, recent studies have attempted to incorporate tactile signals during downstream tasks, enabling pretrained VLAs to interpret tactile feedback. Nevertheless, introducing new modalities during finetuning, which are rarely present in the pretrain stage, may disrupt the pretrained capabilities of VLAs. In addition, the inherently slow inference speed of VLAs hampers real-time responsiveness and limits the effective utilization of tactile feedback for action adjustment. To overcome these challenges, we propose Adaptive Tactile Vision-Language-Action (AT-VLA), which introduces a novel Adaptive Tactile Injection mechanism. This mechanism dynamically determines the appropriate timing and locations for tactile injection, incorporating only when it significantly contributes to action generation, thereby minimizing interference with pretrained representations. Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism, which decouples sensory processing into a slow visual-language stream for low-frequency perceptual reasoning and a fast tactile control stream for high-frequency physical interaction understanding, achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks. The project page is available at: https://sites.google.com/view/at-vla.

Xiaoqi Li, Muhe Cai, Jiadong Xu +5

School of Computer Science, Peking University · PrimeBot · PKU Lab

Contact-Rich Robotic ManipulationVLM AdaptationHaptic FeedbackVision-Language-Action ModelsTactile Sensing for Robotic Manipulation
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May 7, 2026

TouchDrive: Electronics-Free Tactile Sensing Interface for Assistive Grasping

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 grasping. TouchDrive directly converts contact forces into pneumatic feedback through valve-mediated switching, integrating sensing, signal generation, and feedback within a single passive mechanical loop. The system can be employed using a pneumatic normally closed valve, a compressed air tank, sensing element, and haptic feedback actuator without electronics. By delivering tactile cues, TouchDrive empowers users to modulate grasp forces, enabling precise and robust delicate manipulation of compliant and fragile objects. The interface has been validated across diverse robotic platforms, consistently demonstrating reliable performance and practical applicability in assistive grasping tasks, such as handling fruits and everyday items (up to 20 objects).

Jing Xu, Xuezhi Niu, Didem Gurdur Broo +1

Division of Microsystems Technology, Department of Electrical Engineering · Cyber-Physical Systems Lab, Department of Information Technology, Uppsala University, Uppsala, Sweden

Tactile SensingHaptic FeedbackAssistive RoboticsTactile Sensing for Robotic Manipulation
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May 6, 2026

Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception

Tactile perception is key to dexterous manipulation, yet simulating high-resolution elastomer deformation remains computationally prohibitive. Finite element methods (FEM) deliver high fidelity but demand costly remeshing, while Material Point Methods (MPM) suffer from heavy particle-memory tradeoffs. We propose a {reduced-order neural simulation framework} that couples coarse-grained MPM dynamics with an implicit neural decoder to reconstruct sub-particle tactile details from compact latent states. The framework learns a continuous deformation manifold from paired high- and low-resolution simulations, enabling physically consistent, differentiable inference. Compared to the TacIPC, our method achieves over 65% faster simulation and {40% lower memory usage}, while maintaining better geometric fidelity. In tactile rendering and 3D surface reconstruction, our methods further improve accuracy by 25% and produce realistic depth images and surface mesh within a faster inference speed. These results demonstrate that the proposed reduced-order neural model enables high-detail, physically grounded tactile simulation with substantial efficiency gains for robotic interaction and optimization.

Yuhu Guo, Zhikai Shen, Jiasheng Qu +4

The University of Manchester, UK · The Chinese University of Hong Kong, Hong Kong SAR, China · University of Leeds, UK +1

Neural Surrogate ModelingMaterial Point MethodReduced-Order ModelingDifferentiable PhysicsTactile Sensing for Robotic ManipulationDeformable Object Simulation
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May 6, 2026

From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances

High-precision assembly frequently involves tight-tolerance insertions, where even slight pose errors can cause jamming or excessive interaction forces, making robust and safe insertion policies difficult to obtain. This paper proposes a tactile-augmented two-stage method that combines Imitation Learning (IL) and Reinforcement Learning (RL) for precision insertion tasks. In the first stage, IL learns a reaching policy with position generalization that grasps the peg and brings it to the vicinity of the target region. In the second stage, RL executes the insertion and enables recovery from failures during contact-rich interactions. To better exploit tactile feedback, we introduce tactile group sampling to increase coverage of critical contact segments during training, and design a tactile critic to more accurately evaluate policy values, improving insertion performance while maintaining low contact forces. We conduct systematic experiments across five hole geometries and three clearance settings. Results show that our method substantially improves insertion performance across all settings; under the most challenging 0.05,mm clearance, it achieves a 67% success rate while keeping contact forces low, reducing the maximum interaction force by 60% and torque by 44%, thereby validating both effectiveness and safety for precision assembly.

Xinpan Meng, Siyao Huang, JingPu Yang +6

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

RL for RoboticsContact-Rich Robotic ManipulationRobotic InsertionTactile Sensing for Robotic Manipulation
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May 4, 2026

ShapeGrasp: Simultaneous Visuo-Haptic Shape Completion and Grasping for Improved Robot Manipulation

Humans grasp unfamiliar objects by combining an initial visual estimate with tactile and proprioceptive feedback during interaction. We present ShapeGrasp, a robotic implementation of this approach. The proposed method is an iterative grasp-and-complete pipeline that couples implicit surface visuo-haptic shape completion (creation of full 3D shape from partial information) with physics-based grasp planning. From a single RGB-D view, ShapeGrasp infers a complete shape (point cloud or triangular mesh), generates candidate grasps via rigid-body simulation, and executes the best feasible grasp. Each grasp attempt yields additional geometric constraints -- tactile surface contacts and space occupied by the gripper body -- which are fused to update the object shape. Failures trigger pose re-estimation and regrasping using the refined shape. We evaluate ShapeGrasp in the real world using two different robots and grippers. To the best of our knowledge, this is the first approach that updates shape representations following a real-world grasp. We achieved superior results over baselines for both grippers (grasp success rate of 84% with a three-finger gripper and 91% with a two-finger gripper), while improving the 3D shape reconstruction quality in all evaluation metrics used.

Lukas Rustler, Matej Hoffmann

Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.

Robotic Grasping3D ReconstructionRobotic Manipulation3D Shape CompletionTactile Sensing for Robotic Manipulation
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Apr 30, 2026

FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems

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/.

Binghao Huang, Yunzhu Li

Columbia University

Tactile SensingTactile Sensing for Robotic ManipulationRobotic Tactile Sensing
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Apr 29, 2026

Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies

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 underexplored. In this paper, we present a tactile-aware loco-manipulation policy learning pipeline with a hierarchical structure. Our approach has two key components. First, we leverage real-world human demonstrations to train a tactile-conditioned visuotactile high-level policy. This policy predicts not only end-effector trajectories for manipulation, but also the evolving tactile interaction cues that characterize how contact should develop over time. Second, we perform large-scale reinforcement learning in simulation to learn a tactile-aware whole-body control policy that tracks diverse commanded trajectories and tactile interaction cues, and transfers zero-shot to the real world. Together, these components enable coordinated locomotion and manipulation under contact-rich scenarios. We evaluate the system on real-world contact-rich tasks, including in-hand reorientation with insertion, valve tightening, and delicate object manipulation. Compared to vision-only and visuotactile baselines, our method improves performance by 28.54% on average across these tasks.

Pokuang Zhou, Yuhao Zhou, Quan Khanh Luu +7

Purdue University, West Lafayette, IN, USA. · Istituto Italiano di Tecnologia, Genoa, Italy. · Columbia University, New York, NY, USA.

Legged RoboticsRL for RoboticsContact-Rich Robotic ManipulationTactile Sensing for Robotic ManipulationRobotic Loco-Manipulation
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Apr 22, 2026

VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation

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 high-fidelity physical interaction signals, adopts a matrix-style task design to enable systematic learning, and employs automated data collection pipelines covering real-world, demand-driven scenarios to ensure scalability. To further validate the effectiveness of the dataset, we conduct extensive quantitative experiments on cross-modal retrieval as well as real-robot evaluation. Finally, we demonstrate real-world performance through generalizable inference across multiple robots, policies, and tasks.

Qianxi Hua, Xinyue Li, Zheng Yan +4

Humanoid Robot (Shanghai) Co., Ltd., Shanghai, China · Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China · School of Astronautics, Harbin Institute of Technology, Harbin, China

Vision-Based Tactile SensingRobot Manipulation DatasetsTactile SensingBimanual Robotic ManipulationTactile Sensing for Robotic Manipulation
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Apr 22, 2026

ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation

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 interactions with both high fidelity and efficiency. ETac employs a lightweight data-driven deformation propagation model to capture soft-body contact dynamics, achieving high simulation quality and boosting efficiency that enables large-scale policy training. When serving as the simulation backend, ETac produces surface deformation estimates comparable to FEM and demonstrates applicability for modeling real tactile sensors. Then, we showcase its capability in training a blind grasping policy that leverages large-area tactile feedback to manipulate diverse objects. Running on a single RTX 4090 GPU, ETac supports reinforcement learning across 4,096 parallel environments, achieving a total throughput of 869 FPS. The resulting policy reaches an average success rate of 84.45% across four object types, underscoring ETac's potential to make tactile-based skill learning both efficient and scalable.

Zhe Xu, Feiyu Zhao, Xiyan Huang +1

School of Information Science and Technology at ShanghaiTech University, Shanghai 201210, China

Robotic GraspingRobotics SimulationTactile Sensing for Robotic Manipulation
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Apr 14, 2026

Learning Versatile Humanoid Manipulation with Touch Dreaming

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity, and contact-aware interaction under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based lower-body controller that serves as the stability backbone for whole-body execution during complex manipulation. Building on this controller, we develop a VR-based whole-body humanoid data collection system that integrates dexterous hands and tactile sensing for contact-rich manipulation. We then propose Humanoid Transformer with Touch Dreaming (HTD), a multimodal encoder-decoder Transformer that models touch as a core modality alongside multi-view vision and proprioception. HTD is trained in a single stage with behavioral cloning augmented by touch dreaming: in addition to predicting action chunks, the policy predicts future hand-joint forces and future tactile latents, with tactile-latent targets provided by an exponential moving average target encoder without requiring a separate tactile pretraining stage. This encourages the policy to learn contact-aware representations for dexterous manipulation. Across five real-world contact-rich tasks, HTD achieves a 90.9% relative improvement in average success rate over the stronger baseline for each task. Ablation results further show that latent-space tactile prediction is more effective than raw tactile prediction, yielding a 30% relative gain in success rate. These results demonstrate that our touch-dreaming-enhanced learning system enables versatile, high-dexterity humanoid manipulation in the real world. More information and open-source materials are available at humanoid-touch-dream.github.io.

Yaru Niu, Zhenlong Fang, Binghong Chen +8

Carnegie Mellon University · Bosch Center for AI · UT Arlington

Humanoid Loco-ManipulationContact-Rich Robotic ManipulationWhole-Body Robotic ManipulationTactile Sensing for Robotic ManipulationRobot Imitation Learning
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Apr 12, 2026

OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction

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 interaction wrench that are difficult to infer from vision alone. We present OmniUMI, a unified framework for physically grounded robot learning via human-aligned multimodal interaction. OmniUMI synchronously captures RGB, depth, trajectory, tactile sensing, internal grasping force, and external interaction wrench within a compact handheld system, while maintaining collection--deployment consistency through a shared embodiment design. To support human-aligned demonstration, OmniUMI enables natural perception and modulation of internal grasping force, external interaction wrench, and tactile interaction through bilateral gripper feedback and the handheld embodiment. Built on this interface, we extend diffusion policy with visual, tactile, and force-related observations, and deploy the learned policy through impedance-based execution for unified regulation of motion and contact behavior. Experiments demonstrate reliable sensing and strong downstream performance on force-sensitive pick-and-place, interactive surface erasing, and tactile-informed selective release. Overall, OmniUMI combines physically grounded multimodal data acquisition with human-aligned interaction, providing a scalable foundation for learning contact-rich manipulation.

Shaqi Luo, Yuanyuan Li, Youhao Hu +7

Beijing Academy of Artificial Intelligence · MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences +3

Robot LearningContact-Rich Robotic ManipulationRobotic Data CollectionForce-Controlled Robotic ManipulationTactile Sensing for Robotic ManipulationHuman-Robot Interaction
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Apr 1, 2026

How to Train your Tactile Model: Tactile Perception with Multi-fingered Robot Hands

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 for each new sensor due to differences in lens properties, illumination, and sensor wear. Here we introduce TacViT, a novel tactile perception model based on Vision Transformers, designed to generalize on new sensor data. TacViT leverages global self-attention mechanisms to extract robust features from tactile images, enabling accurate contact property inference even on previously unseen sensors. This capability significantly reduces the need for data collection and retraining, accelerating the deployment of new sensors. We evaluate TacViT on sensors for a five-fingered robot hand and demonstrate its superior generalization performance compared to CNNs. Our results highlight TacViTs potential to make tactile sensing more scalable and practical for real-world robotic applications.

Christopher J. Ford, Kaichen Shi, Laura Butcher +2

School of Engineering Mathematics and Technology and Bristol Robotics Laboratory, University of Bristol, UK

Tactile SensingTactile Sensing for Robotic ManipulationVision-Based Tactile SensingVision Transformer
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Mar 29, 2026

Probe-to-Grasp Manipulation Using Self-Sensing Pneumatic Variable-Stiffness Joints

Grasping deformable objects with varying stiffness remains a significant challenge in robotics. Estimating the local stiffness of a target object is important for determining an optimal grasp pose that enables stable pickup without damaging the object. This paper presents a probe-to-grasp manipulation framework for estimating the relative stiffness of objects using a passive soft-rigid two-finger hybrid gripper equipped with self-sensing pneumatic variable-stiffness joints. Each finger of the gripper consists of two rigid links connected by a soft pneumatic ring placed at the joint, enabling both compliant interaction and controllable joint stiffness via internal pressurization. By measuring the pressure inside the pneumatic ring, we can estimate the interaction force during contact. Building on this, we propose a practical probing strategy to infer relative object stiffness by correlating the estimated normal force with known gripper closing displacement. We validate the self-sensing model through stiffness characterization experiments across bending angles and pressure ranges, and demonstrate stiffness-aware probing-and-grasping in real-life applications: selecting grasp locations on fruits with spatially varying stiffness. The proposed system offers a minimal, low-cost sensing approach for stiffness-aware soft manipulation while retaining probing and grasping capability.

Ngoc Duy Tran, Yeman Fan, Feng Dai +5

Robotic GraspingDeformable Object ManipulationSoft RoboticsTactile Sensing for Robotic Manipulation
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