Haptic Feedback for Robot Teleoperation

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20 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Oct 16, 2026

Evaluating Lightweight Contact Feedback in Dual-Operator Collaborative Humanoid Teleoperation

Dual-operator humanoid teleoperation enables collaborative manipulation through two separate robots, but operators lack direct awareness of robot-object contact that is normally available during physical interaction. To address this, we present eXtended Reality Teleoperation (XRT), an extended reality teleoperation framework that returns a robot's fingertip contact through lightweight vibrotactile and/or auditory feedback, and a synchronized dual-humanoid testbed built on it for two operators. Sixteen dyads performed a collaborative crate lift-and-flip under vision only and the three feedback conditions in a counterbalanced within-subject design with verified feedback-channel delivery. Three results follow. First, operators rated our finger-localized vibrotactile display as more effective than our open-ear sonification: all 32 ranked it above the sonification, 18 ranked it most effective, and its cues were rated far easier to perceive and interpret than the auditory cues. Second, completion time, failures, workload, discomfort, and partner awareness showed no detected condition differences, with wide completion-time intervals and smaller meaningful effects unresolved. Third, performance changed substantially with practice, especially on the harder flip-over task: completion time improved across blocks, and attempt-to-attempt variation within a dyad (SD x1.51) was larger than the largest condition contrast (x1.16). Exploratory analyses detected no difference in immediate grip or motion responses between feedback conditions. For novice collaborative teleoperation, our fingertip vibrotactile cues were rated more effective and easier to interpret than our auditory cues, while their effect on task performance remains unresolved.

Austin Graves, Chuizheng Kong, Yunho Cho +4

1Symbiotic and Augmented Intelligence Lab, Georgia Institute of Technology, Atlanta, GA, USA. · 2Robot Danger Lab, Georgia Institute of Technology, Atlanta, GA, USA.

Robot Teleoperation Haptic Feedback for Robot Teleoperation Contact-Rich Robotic Manipulation Humanoid Robotics

Oct 14, 2026

FOMI: A Force-Feedback Manipulation Interface for Teaching Contact-Rich Manipulation

Robot-free collection with handheld interfaces enables scalable acquisition of human demonstrations for policy learning. However, existing handheld interfaces often provide limited intuitive force feedback, making contact-rich demonstration collection challenging for human operators. To address this, we present FOMI, a force-feedback manipulation interface with a scissor-shaped mechanism, which transmits object interaction forces directly to the operator's hands through actuation links without force sensors. This intuitive force feedback supports real-time action adjustment during robot-free collection of contact-rich demonstrations. Around FOMI, we build a demonstration collection system that integrates sensor calibration, data acquisition, and synchronization for robot-free collection. By further mapping pose changes and gripper opening of FOMI to robot end-effector poses and gripper commands, the system also supports teleoperation. Across four contact-rich tasks, FOMI improves collection efficiency by approximately 29% and 18% over the best baselines in robot-free collection and teleoperation, respectively. Demonstrations collected with FOMI in both modes enable policies to perform challenging contact-rich manipulation tasks with high success rates.

Yishu Shen, Yiming Luo, Ruilin Wang +7

The Hong Kong University of Science and Technology · The University of Hong Kong · Robbyant +1

Haptic Feedback for Robot Teleoperation Robotic Data Collection Contact-Rich Robotic Manipulation Robot Imitation Learning Robot Teleoperation

Oct 10, 2026

LETS: Low-Overhead External-Torque Sensing for Force-Feedback Bilateral Teleoperation

Bilateral teleoperation with force feedback is becoming increasingly important, and the quality of external-torque estimation is critical to this process. However, manufacturer-provided disturbance-torque estimates on commercial robotic arms are often inaccurate, while the use of end-effector force/torque sensors adds cost and payload. We propose LETS, a physics-guided learning approach that assigns rigid-body dynamics to recursive Newton-Euler inverse dynamics and reserves learning for only the robot-specific residual. LETS uses only the robot's URDF model and joint feedback, deliberately excluding control commands and thereby decoupling external-torque estimation from control-command inputs. It trains on 12 minutes of free-motion data in under one minute, without requiring a dedicated end-effector six-axis force/torque sensor. In bilateral teleoperation, LETS achieves real-time external-torque estimation that enables stable force feedback. Its free-space error is 69% lower than the manufacturer-provided estimate, substantially reducing coupled oscillations between leader and follower; its contact error is 28% lower, yielding more accurate haptic feedback.

Zonghao Guo, Dongchen Guan, Yubo Wang +1

Institute of Automation, Chinese Academy of Sciences, China. · School of Artificial Intelligence, University of Chinese Academy of Sciences, China. · Beijing Amigos Robot Technology Co., Ltd., China.

Robot Teleoperation Haptic Feedback for Robot Teleoperation Physics-Informed ML

Oct 4, 2026

TacGELLO: Tactile Contact Feedback for a 3D-Printed Teleoperation Leader

Passive leader arms transmit an operator's motion without returning remote contact or load cues. TacGELLO uses the existing servos of a 3D-printed leader for tactile contact feedback at the gripper trigger and current-based load cues at the arm joints. AnySkin contact detection activates a trigger position hold, while changes in follower joint current adjust leader-servo output limits to command resistance during lifting. Across six UR3 sessions with one operator, sent-command tracking error was 0.15--0.36

^\circ

RMSE, with estimated trajectory lag of 82--98,ms. Tactile onset was logged in 60 of 62 segmented TacGELLO grasp intervals. Median commanded closure after tactile or width-based contact references was 0.7--2.1,mm with TacGELLO and 13.0--13.9,mm with the original interface.

Younsoo Kim, Sangmin Song, Sarath Kodagoda +2

University of Technology Sydney, NSW, Australia · Western Sydney University, NSW, Australia

Tactile Sensing Robot Teleoperation Haptic Feedback for Robot Teleoperation Haptic Feedback Robotic Manipulation

Sep 27, 2026

Surgical Master Console Using General-Purpose Robot Arms and a Separable Articulated Distal Interface: Porcine In-Vivo Evaluation

High-performance surgical master consoles offer intuitive articulated manipulation but are expensive and difficult to reproduce, while accessible commercial haptic devices lack built-in interfaces for surgical wrist articulation and continuous grasp. We present a laparoscopic master in which general-purpose robot manipulators provide the programmable base and surgical-specific interaction is concentrated in a separable distal adapter. Each 6-DoF arm carries a custom 2-DoF direct-drive wrist, continuous grasp sensing, and clutch-based workspace management; the complete bimanual hardware costs approximately US$5,500, below common bimanual commercial haptic references. To support sustained hand-guided use, we characterize current scaling, gravity, and break-away friction and use them for posture-dependent gravity compensation and motion-gated assistance, reducing continuous actuation and thermal loading. A common master state drives either Isaac Sim or an RCM-constrained dual-FR3 platform. Master-state publication to FR3 target update adds at most 8 ms, and tip tracking shows 0.31-0.66 mm mean delay-aligned residuals. In a porcine cholecystectomy experiment, five common operator-side metrics and hand/tip-speed distributions were compared descriptively with human in-vivo telemetry from 120 clinical Versius recordings of 99 cholecystectomy procedures; no interruption originated from the master console, and no abnormal clinical signs were recorded during the 7-day postoperative observation period. These results demonstrate a surgical master built from general-purpose robot arms with surgical-specific interaction concentrated in a separable distal interface.

Minsung Kim, Seonho Shim, Dongho Yee +9

Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea. · Rosota Inc., Seoul, Republic of Korea. · Department of Mechanical Engineering, Seoul National University, Seoul, Republic of Korea. +6

Haptic Feedback for Robot Teleoperation Surgical Robotics Robot Teleoperation Bimanual Robotic Manipulation Laparoscopic Surgery

Sep 16, 2026

DITTO: Dexterous Interface for Transparent TeleOperation

Collecting data for manipulation with high-DOF hands is challenging, as interfaces must capture rich hand motion while rendering the contact interactions essential for precise manipulation. Existing data collection approaches face a trade-off: teleoperation ensures deployment consistency but lacks force feedback, while handheld (in-the-wild) systems provide natural force transparency but introduce a visual embodiment gap at deployment. We present DITTO, a Dexterous Interface for Transparent TeleOperation, which resolves this through the anatomically informed co-design of a dexterous 7-DOF robotic hand and a kinematically equivalent motorized exoskeleton. A 1-to-1 actuator mapping between the exoskeleton and robotic hand enables handheld (in-the-wild) data collection and bilateral teleoperation with joint-level force feedback unified in a single platform. We demonstrate that the DITTO exoskeleton spans the operator's natural index-to-thumb workspace, and showcase DITTO's dexterous capabilities via learned policies on contact-rich tasks.

Joaquin Palacios, Katelyn Lee, Cheng Zhang +2

Dept. of Mechanical Engineering, Columbia University · Dept. of Computer Science, Columbia University · Dept. of Computer Science, Stanford University

Robot Teleoperation Robotic Data Collection Robotic Hand Design Dexterous Manipulation Haptic Feedback for Robot Teleoperation

Aug 22, 2026

Remote Surfaces at Your Fingertips: Electrovibration-Based Tactile Feedback for Robot Teleoperation via Touchscreen Interfaces

Enabling operators to perceive and interact with remote environments naturally is a fundamental challenge in robotic teleoperation. This is especially critical in tasks involving physical interaction, where real-time haptic awareness improves operational safety and effectiveness. Existing kinesthetic haptic feedback methods suffer from instability during rigid surface contacts and remain sensitive to communication delays, while visual cue-based force feedback imposes additional cognitive load and limits sustained situational awareness. This work presents a teleoperation interface that conveys remote surface interactions to the operator through electrovibration-based tactile feedback, enabling naturally mapped force reflection while avoiding the stability issues associated with kinesthetic feedback and the latency limitations of mechanical actuators. A user study (N=21) evaluated interface usability, sense of presence, and operator workload under two force reflection conditions: visual feedback and electrovibration-based tactile feedback. Characterisation experiments further assessed path-following accuracy and response time across both conditions. Results show that tactile feedback significantly reduced response time by 15.35% (p=0.002, d=0.96) and increased the sense of presence by 31% (p<0.001, d=0.90) compared to visual feedback, while imposing comparable workload and usability across both conditions. These findings demonstrate that electrovibration-based tactile feedback is a viable and effective modality for robot teleoperation, improving operator responsiveness and sense of presence in contact-rich manipulation tasks, with direct applicability to safety-critical domains such as nuclear maintenance.

Alperen Kenan, Juan José García Cárdenas, Adriana Tapus +2

Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom · Computer Science and System Engineering Department (U2IS), Autonomous Systems and Robotics Lab, ENSTA, Institut Polytechnique de Paris, Paris, France · Kempten University of Applied Sciences, Kempten, Germany

Haptic Feedback for Robot Teleoperation Robot Teleoperation Haptic Feedback Contact-Rich Robotic Manipulation

Aug 20, 2026

Wave-Based Bilateral Teleoperation between Nonlinear Manipulators with Direct Contact Force Feedback

We study bilateral teleoperation between nonlinear, multi-DOF robotic manipulators in the presence of constant communication delays. Unlike classical wave-transformation architectures that transmit a coordinating force, we consider the case where the environmental force is reflected to the master side to enhance teleoperation transparency. Since direct contact force feedback might destabilize the closed-loop system, we first develop a passivity-shortage characterization for the Euler--Lagrange remote system using a linear matrix inequality (LMI) approach. An upper strictly passive communication law is then employed to compensate for the computed passivity shortage so that the closed-loop stability under delays as well as position and force synchronization are preserved under appropriate conditions. Simulations with nonlinear 2-DOF robotic manipulators in different settings illustrate our approach.

G. Q. Bao Tran, Takanori Miyoshi, Ho Duc Tho

Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA · Department of System Safety, Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Niigata, Japan · Department of Control Engineering and Automation, HCMC University of Technology (HCMUT), HCMC, Vietnam

Haptic Feedback for Robot Teleoperation Robot Teleoperation Passivity-Based Control

Aug 19, 2026

The Missing Touch: Spatially Distributed Tactile Feedback Brings Teleoperation Closer to Human Dexterity

A fundamental challenge in robotic teleoperation is enabling an operator to control a remote robot as effortlessly and intuitively as their own hands. Despite the growing use of teleoperation to collect demonstration data for training autonomous robot policies, teleoperated robot performance still falls significantly short of human dexterity, even for basic tasks. Here, we present evidence that a key factor contributing to this performance gap is the absence of spatially distributed tactile feedback. Using a two-degree-of-freedom (DoF) bilateral force-feedback telemanipulator paired with a 32-DoF tactile fingertip display, we show that operator performance improves significantly when localized deformations on the remote manipulator are faithfully reproduced on the operator's fingertip. In a series of teleoperation tasks, reproducing distributed contact information not only accelerated task performance but also brought teleoperated movements closer to natural human behavior by minimizing corrective actions and task completion steps, thereby reducing the deviation between teleoperated and natural trajectories by 29

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79%. Furthermore, we found that increasing the resolution of the tactile feedback

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by refining how finely the measured displacements were quantized for reproduction

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compressed the state-space distribution of teleoperated motions, which has been associated with improved training outcomes for autonomous robot policies. Together, these results suggest that spatially distributed tactile feedback is essential for closing the gap between human and teleoperated dexterity and training the next generation of autonomous robots.

Rohan Kota, Gregory Reardon, J. Edward Colgate

Center for Robotics and Biosystems, Northwestern University, Evanston, IL.

Robot Teleoperation Haptic Feedback for Robot Teleoperation Haptic Feedback

Aug 17, 2026

ViHaTeleop: A Low-Cost, Lightweight Visual-Haptic Teleoperation System for Dexterous Manipulation Learning

Learning from demonstration is a promising approach for dexterous manipulation, but collecting high-quality contact-critical demonstrations remains difficult with low-cost teleoperation hardware. We present ViHaTeleop, a lightweight (0.7 kg), low-cost ($550) visual-haptic teleoperation system with SLAM-based wrist tracking, camera-based hand tracking, and finger-wise vibrotactile feedback through Linear Resonant Actuators (LRA). The system includes several design choices (LED illumination, fisheye hand camera, and tactile-aware retargeting constraints) and is deployed on Franka + LEAP Hand + 9DTact in both real and simulated environments. Under matched with/without-haptic conditions with nine participants across six contact-critical tasks, haptics improved success rates across all tasks (+2.2 to +15.6 percentage points), while completion-time effects were task-dependent. Subjective ratings showed significant gains in contact clarity and grasp confidence in both simulation and real-world settings (Wilcoxon signed-rank,

p<0.05

). We also integrate a lightweight depth-camera-based tactile proxy in Isaac Sim, enabling a full pipeline from multi-modal demonstration collection to visual-tactile policy training. Preliminary downstream validation by training visual-tactile policies from collected demonstrations shows tactile cues benefit contact-critical subtasks (peg-in-hole: +17 percentage points over vision-only).

Fucai Zhu, Yanhou Lai, Paul Maestre +1

Graduate School of Information Sciences, Tohoku University, Sendai, Japan

Robot Teleoperation Haptic Feedback for Robot Teleoperation Haptic Feedback Contact-Rich Robotic Manipulation Robot Imitation Learning

Aug 3, 2026

Semantic Haptic Feedback Enhances Dexterous Robotic Teleoperation

In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic design for a common pick and place teleoperation task and compare semantic haptics to other teleoperation feedback approaches including sensory haptics and visual feedback. Results suggest that while semantic haptics performs similarly as other feedback in unimanual tasks, it achieves superior performance in bimanual tasks, with reduced task workload, increased situational awareness, and overall preference.

Bingjian Huang, Sahar Aseeri, Jonas Schmidtler +9

Robot Teleoperation Haptic Feedback Robotic Manipulation Haptic Feedback for Robot Teleoperation

Jun 13, 2026

A Bilateral Teleoperation Framework for Dexterous Manipulation

Dexterous teleoperation requires precise arm-hand coordination, low-latency feedback, and robust interaction in real-world contact-rich environments. This paper presents a modular bilateral teleoperation framework that integrates operator-side input interfaces with a robot-side dexterous hand and compliant robotic arm in a unified control architecture. The system supports position-based hand retargeting, differential arm control, multi-scale haptic feedback, and shared control for stable manipulation. We validate the framework through a real-world dexterous manipulation task, highlighting coordinated arm-hand control and contact-aware interaction. Beyond feasibility, we identify key design insights related to cross-embodiment mismatch, haptic feedback granularity, and shared control. The proposed platform provides a practical teleoperation system and a foundation for collecting high-quality demonstrations for future learning-from-demonstration research.

Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang +10

The University of Texas at Austin, Austin, TX, USA · Sony Group Corporation, Tokyo, Japan · Meta Reality Labs Research, Redmond, WA, USA

Dexterous Manipulation Contact-Rich Robotic Manipulation Robot Teleoperation Haptic Feedback for Robot Teleoperation

Jun 12, 2026

Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback

For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact. However, the majority of existing data collection pipelines still lack the ability to capture force and torque data for learning active compliant policies. In this paper, we present Universal Manipulation Exoskeleton (UME), an upper-limb exoskeleton that provides real-time haptic torque feedback while recording whole-arm configurations and joint torque signals for teleoperation. With transparent torque feedback, human operators can even unsheathe kinematically constrained objects while blindfolded. UME is low-cost, lightweight, and portable. Equipped with an embedded IMU, it enables teleoperation for mobile manipulation. With our proposed universal retargeting algorithm, UME can teleoperate a range of robots, including the 7DoF OpenArm, 7DoF Franka, and 6DoF X-ARM. We demonstrate that this combination of capabilities enables learning bimanual, whole-body, and active compliant policies that operate effectively in highly constrained spaces. The learned robust autonomous policies achieve high success rates across a variety of tasks, including long-horizon mobile manipulation, force-mediated box flipping, visually occluded box pushing, and space-constrained tabletop manipulation. Videos, code, and additional information can be found at https://ume-exo.github.io.

Litian Liang, Jingxi Xu, Xinda Qi +7

1Ant Group · 2Stanford University

Long-Horizon Robotic Manipulation Robot Teleoperation Cross-Embodiment Robot Learning Robotic Manipulation Upper-Limb Exoskeletons Haptic Feedback for Robot Teleoperation

Jun 12, 2026

The N2D Haptic Glove: A Multi-Finger Glove for 2D Directional Force Feedback for Contact Rich Manipulation

Humans rely on directional fingertip forces to probe and regulate contact during manipulation, yet most wearable haptic gloves render only vibration or single-axis force, leaving force direction ambiguous. Without directional cues, users must infer contact force from vision alone, often leading to over-pressing, inconsistent control, and reduced precision in robotic teleoperation. We present the N2D Haptic Glove, a multi-finger wearable device that renders planar flexion-extension fingertip forces using capstan-drive transmissions for high-transparency force feedback. Through benchtop validations and a user study involving haptic teleoperation of a robotic arm and hand, we demonstrate that compared to visual-only and single-axis haptic baselines, planar fingertip feedback significantly reduces contact force error during precise manipulation, improves trial-to-trial consistency, and enhances overall user experience in axial probing tasks. These findings establish the N2D Haptic Glove and directional finger-based haptics devices as a promising modality for contact-rich teleoperation, immersive virtual reality simulations, and robot learning from demonstrations. N2D Haptic Glove's hardware and software system will be fully open-sourced at this https URL.

Yao-Ting Huang, Jake Honma, Omar Hernandez +4

Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA. · Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA 92093, USA. · Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.

Contact-Rich Robotic Manipulation Robot Teleoperation Haptic Feedback Haptic Feedback for Robot Teleoperation

Jun 3, 2026

HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning

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 its established role in demonstration quality and manipulation stability. In this work, we present HapTile, a contact-grounded visuotactile manipulation dataset that advances beyond vision-only trajectory datasets by embedding physical interaction sensing at two levels: fingertip tactile feedback at the robot end-effector, and haptic-informed demonstrations at the teleoperator side. The data collection platform integrates haptic feedback directly into the teleoperation controller, enabling the operator to perceive contact interactions in real time. It is built around a standard and reproducible robotic system equipped with custom-designed fingertip tactile sensors. The dataset comprises everyday manipulation tasks spanning a broad range of contact-rich skills, including pick-and-place, folding, pressing, stacking, and other routine activities. Each task is paired with language instructions that condition the policy on the manipulation objective, together with synchronized visuotactile observations and action trajectories. In addition, we provide a benchmarking study on contact-rich policy learning using two baseline models to evaluate the effectiveness of the proposed contact-grounded dataset. The dataset and additional details are available on our website: haptile-dataset.github.io.

Amirhosein Alian, Yongqiang Zhao, Shiyi Gu +5

King’s College London, UK · Huawei, Noah’s Ark Lab, UK · University College London, UK

Contact-Rich Robotic Manipulation Robot Teleoperation Haptic Feedback for Robot Teleoperation Robotic Manipulation Robot Imitation Learning Robot Manipulation Datasets

Mar 31, 2026

HapCompass: A Rotational Haptic Device for Contact-Rich Robotic Teleoperation

The contact-rich nature of manipulation makes it a significant challenge for robotic teleoperation. While haptic feedback is critical for contact-rich tasks, providing intuitive directional cues within wearable teleoperation interfaces remains a bottleneck. Existing solutions, such as non-directional vibrations from handheld controllers, provide limited information, while vibrotactile arrays are prone to perceptual interference. To address these limitations, we propose HapCompass, a novel, low-cost wearable haptic device that renders 2D directional cues by mechanically rotating a single linear resonant actuator (LRA). We evaluated HapCompass's ability to convey directional cues to human operators and showed that it increased the success rate, decreased the completion time and the maximum contact force for teleoperated manipulation tasks when compared to vision-only and non-directional feedback baselines. Furthermore, we conducted a preliminary imitation-learning evaluation, suggesting that the directional feedback provided by HapCompass enhances the quality of demonstration data and, in turn, the trained policy. We release the design of the HapCompass device along with the code that implements our teleoperation interface: https://ripl.github.io/HapCompass/.

Xiangshan Tan, Jingtian Ji, Tianchong Jiang +2

Toyota Technological Institute at Chicago (TTIC) · University of Chicago

Haptic Feedback for Robot Teleoperation Haptic Feedback Robot Teleoperation Contact-Rich Robotic Manipulation

Mar 30, 2026

Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation

Teleoperation is a key approach for collecting high-quality, physically consistent demonstrations for robotic manipulation. However, teleoperation for dexterous manipulation remains constrained by: (i) inaccurate hand-robot motion mapping, which limits teleoperated dexterity, and (ii) limited tactile feedback that forces vision-dominated interaction and hinders perception of contact geometry and force variation. To address these challenges, we present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback, enabling effective tactile-in-the-loop dexterous teleoperation. For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking with joint angle estimation errors below 1 degree. Meanwhile, to restore tactile sensation, TAG equips each finger with a 32-actuator tactile array within a compact 2 cm^2 module, allowing operators to directly feel physical interactions at the robot end-effector through spatial activation patterns. Through real-world teleoperation experiments and user studies, we show that TAG enables reliable real-time perception of contact geometry and dynamic force, improves success rates in contact-rich teleoperation tasks, and increases the reliability of demonstration data collection for learning-based manipulation.

Feiyu Jia, Xiaojie Niu, Sizhe Yang +5

University of Science and Technology of China · Shanghai AI Laboratory · The Chinese University of Hong Kong +1

Haptic Feedback for Robot Teleoperation Robot Teleoperation Robotic Tactile Sensing Robotic Data Collection

Mar 6, 2026

CDF-Glove: A Cable-Driven Force Feedback Glove for Dexterous Teleoperation

High-quality teleoperated demonstrations are a primary bottleneck for imitation learning (IL) in dexterous manipulation. However, haptic feedback provides operators with real-time contact information, enabling real-time finger posture adjustments, and thereby improving demonstration quality. Existing dexterous teleoperation platforms typically omit haptic feedback and remain bulky and expensive. We introduce CDF-Glove, a lightweight and low cost cable-driven force-feedback glove. The real-time state is available for 20 finger degrees of freedom (DoF), of which 16 are directly sensed and 4 are passively coupled (inferred from kinematic constraints). We develop a kinematic model and control stack for the glove, and validate them across multiple robotic hands with diverse kinematics and DoF. The CDF-Glove achieves distal joint repeatability of 0.4 degrees, and delivers about 200 ms force feedback latency, yielding a 4x improvement in task success rate relative to no-feedback teleoperation. We collect two bimanual teleoperation datasets, on which we train and evaluate Diffusion Policy baselines. Compared to kinesthetic teaching, the policies trained in our teleoperated demonstrations increase the average success rate by 55% and reduce the mean completion time by approximately 15.2 seconds (a 47.2% relative reduction). In particular, the CDF-Glove costs approximately US$230. The code and designs are released as open source at https://cdfglove.github.io/.

Huayue Liang, Ruochong Li, Yaodong Yang +3

Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · PKU-Psibot Joint Lab, Peking 100871, China · Department of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

Robot Teleoperation Haptic Feedback for Robot Teleoperation Robot Imitation Learning

Mar 5, 2026

TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback

Recent advances in teleoperation have enabled sophisticated manipulation of dexterous robotic hands, with most systems concentrating on guiding finger positions to achieve desired grasp configurations. However, while accurate finger positioning is essential, it often overlooks the equally critical task of grasp force modulation, vital for handling objects of diverse hardness, texture, and shape. This limitation poses a significant challenge for users, especially individuals with upper limb disabilities who lack natural tactile feedback and rely on indirect cues to infer appropriate force levels. To address this gap, We present the tactile enhanced grasping assistant (TEGA), a closed loop assistive teleoperation framework that fuses EMG based intent2force inference with visuotactile sensing mapped into real time vibrotactile feedback via a wearable haptic vest, enabling intuitive, proportional force adjustment during manipulation. A wearable haptic vest delivers real time tactile feedback, allowing users to dynamically refine grasp force during manipulation. User studies confirm that the system substantially improves grasp stability and task success, underscoring its potential for assistive robotic applications.

Hengxu You, Tianyu Zhou, Fang Xu +2

All authors are with the Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USA. · All authors are with Nvidia

Haptic Feedback for Robot Teleoperation Robotic Grasping Assistive Robotics Robot Teleoperation Force-Controlled Robotic Manipulation Robotic Tactile Sensing

Feb 25, 2026

Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control

Robotic systems can enhance the amount and repeatability of physically guided motor training. Yet their real-world adoption is limited, partly due to non-intuitive trainer/therapist-trainee/patient interactions. To address this gap, we present a haptic teleoperation system for trainers to remotely guide and monitor the movements of a trainee wearing an arm exoskeleton. The trainer can physically interact with the exoskeleton through a commercial handheld haptic device via virtual contact points at the exoskeleton's elbow and wrist, allowing intuitive guidance. Thirty-two participants tested the system in a trainer-trainee paradigm, comparing our haptic demonstration system with conventional visual demonstration in guiding trainees in executing arm poses. Quantitative analyses showed that haptic demonstration significantly reduced movement completion time and improved smoothness, while speech analysis using large language models for automated transcription and categorization of verbal commands revealed fewer verbal instructions. The haptic demonstration did not result in higher reported mental and physical effort by trainers compared to the visual demonstration, while trainers reported greater competence and trainees lower physical demand. These findings support the feasibility of our proposed interface for effective remote human-robot physical interaction. Future work should assess its usability and efficacy for clinical populations in restoring clinicians' sense of agency during robot-assisted therapy.

Beatrice Luciani, Alex van den Berg, Matti Lang +2

Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands · Department of Rehabilitation Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.

Upper-Limb Exoskeletons Human-Robot Interaction Robot Teleoperation Assistive Robotics Haptic Feedback for Robot Teleoperation Haptic Feedback