TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback
- URL: http://arxiv.org/abs/2603.05552v1
- Date: Thu, 05 Mar 2026 04:12:55 GMT
- Title: TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback
- Abstract summary: We present the tactile enhanced grasping assistant (TEGA)<n>A closed loop assistive teleoperation framework fuses EMG based intent2force inference with visuotactile sensing mapped into real time vibrotactile feedback.<n>A wearable haptic vest delivers real time tactile feedback, allowing users to dynamically refine grasp force during manipulation.
- Score: 1.3861223044153617
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- XRoboToolKit-T: Teleoperation with High Stability and Precision with Tactile Sensing for Contact-rich Manipulation [10.905283789732872]
We propose a versatile teleoperation system with tactile-driven assistance to enable contact-rich manipulation.<n>The proposed XRoboToolKit-T teleoperation system incorporates a tactile-informed force control architecture.<n>The system achieves higher data collection efficiency and improved manipulation stability compared to state-of-the-art teleoperation without assistance.
arXiv Detail & Related papers (2026-09-14T23:24:22Z) - AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion [60.79507611737292]
Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback.<n>Existing robotic grasping approaches rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact.<n>We propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement.
arXiv Detail & Related papers (2026-08-06T16:29:11Z) - VTAP Gripper: Synergizing Fingertip Sensing and a Visuo-Tactile Active Palm for Dexterous In-Hand Manipulation [22.38178317703772]
This paper presents a tactile-reactive gripper that integrates a Visuo-Tactile Active Palm (VTAP) and compliant, reconfigurable fingers equipped with array sensors.<n>The design exploits structured finger-palm synergy and multi-modal tactile perception to achieve both robust grasping and fine manipulation.
arXiv Detail & Related papers (2026-07-16T20:41:00Z) - The N2D Haptic Glove: A Multi-Finger Glove for 2D Directional Force Feedback for Contact Rich Manipulation [11.79647885994712]
Humans rely on directional fingertip forces to probe and regulate contact during manipulation.<n>Most wearable haptic gloves render only vibration or single-axis force, leaving force direction ambiguous.<n>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.
arXiv Detail & Related papers (2026-06-12T03:59:33Z) - AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning [56.20593626288444]
AetheRock is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip.<n>We propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning.
arXiv Detail & Related papers (2026-06-08T17:38:57Z) - Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation [29.061513339025026]
We present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback.<n>For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking.<n>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.
arXiv Detail & Related papers (2026-03-30T15:04:13Z) - HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing [1.5861606459586157]
We argue that tactile-aware manipulation can be learned offline and deployed without direct haptic feedback at inference.<n>We present HapticVLA, which proceeds in two tightly coupled stages: Safety-Aware Reward-Weighted Flow Matching (SA-RWFM) and Tactile Distillation (TD)
arXiv Detail & Related papers (2026-03-16T13:24:58Z) - ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation [75.39847750457626]
ForceVLA2, an end-to-end vision-language-action framework, equips robots with hybrid force-position control and explicit force awareness.<n>To support learning and evaluation, we construct ForceVLA2-Dataset, containing 1,000 trajectories over 5 contact-rich tasks.<n>Experiments show that ForceVLA2 substantially improves success rates and reliability in contact-rich manipulation.
arXiv Detail & Related papers (2026-03-16T12:03:58Z) - Symmetry-Aware Fusion of Vision and Tactile Sensing via Bilateral Force Priors for Robotic Manipulation [7.104060092661104]
We propose a Cross-Modal Transformer (CMT) for visuo-tactile fusion.<n>CMT integrates wrist-camera observations with tactile signals through structured self- and cross-attention.<n>Experiments on the TacSL benchmark show that CMT with symmetry regularization achieves a 96.59% insertion success rate.
arXiv Detail & Related papers (2026-02-14T09:19:48Z) - UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
arXiv Detail & Related papers (2026-02-01T11:03:01Z) - TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance [53.35296919674763]
TouchGuide is a novel cross-policy visuo-tactile fusion paradigm that fuses modalities within a low-dimensional action space.<n>touchGuide operates in two stages to guide a pre-trained diffusion or flow-matching visuomotor policy.<n>To facilitate TouchGuide training with high-quality and cost-effective data, we introduce TacUMI, a data collection system.
arXiv Detail & Related papers (2026-01-28T04:22:47Z) - CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
arXiv Detail & Related papers (2025-12-16T18:56:04Z) - DiG-Net: Enhancing Quality of Life through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics [2.625826951636656]
We introduce a novel approach designed specifically for assistive robotics, enabling dynamic gesture recognition at extended distances of up to 30 meters.<n>Our proposed Distance-aware Gesture Network (DiG-Net) effectively combines Depth-Conditioned Deformable Alignment (DADA) blocks with Spatio-Temporal Graph modules.<n>By effectively interpreting gestures from considerable distances, DiG-Net significantly enhances the usability of assistive robots in home healthcare, industrial safety, and remote assistance scenarios.
arXiv Detail & Related papers (2025-05-30T16:47:44Z) - ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation [62.58034332427291]
ForceVLA is a novel end-to-end manipulation framework.<n>It treats external force sensing as a first-class modality within VLA systems.
arXiv Detail & Related papers (2025-05-28T09:24:25Z) - ForceGrip: Reference-Free Curriculum Learning for Realistic Grip Force Control in VR Hand Manipulation [0.10995326465245926]
We present ForceGrip, a deep learning agent that synthesizes realistic hand manipulation motions.<n>We employ a three-phase curriculum learning framework comprising Finger Positioning, Intention Adaptation, and Dynamic Stabilization.<n>Our evaluations reveal ForceGrip's superior force controllability and plausibility compared to state-of-the-art methods.
arXiv Detail & Related papers (2025-03-11T05:39:07Z) - Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation [58.95799126311524]
Humans can accomplish contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces.<n>Existing visual imitation learning approaches rely on action chunking to model complex behaviors.<n>We introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality.
arXiv Detail & Related papers (2025-03-04T18:58:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.