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

2026-03-05

AI Summary

This work addresses the limited fine-grained grip force control and lack of intuitive haptic feedback in existing teleoperation systems for dexterous hand grasping, particularly for users with limb impairments who must adapt to variations in object hardness, texture, and shape. The authors propose a closed-loop assistive teleoperation framework that, for the first time, integrates electromyographic (EMG) intent decoding with vision–tactile multimodal perception and delivers proportional, intuitive grip force feedback via a wearable vibrotactile vest. This approach establishes a closed-loop mechanism mapping user intent directly to force output, significantly enhancing both grip force perception and control accuracy. User studies demonstrate improved grasp stability and task success rates, confirming the system’s practical utility and innovative potential in assistive robotics applications.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• grasp force modulation
• tactile feedback
• assistive robotics
• teleoperation
• haptic feedback

Innovation

• sensor fusion
• closed-loop haptic feedback
• EMG-based intent inference
• visuotactile sensing
• assistive teleoperation