AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning

2026-06-08

AI Summary

This work addresses the inefficiency in robotic learning and perceptual inconsistency arising from the difficulty of co-integrating force and tactile sensing in wearable systems. To overcome these challenges, we propose AetheRock, a forearm-worn robot teaching system that synergistically combines vision, touch, and fingertip pressure sensing. AetheRock features a novel, modular, and easily manufacturable GelSlim-MiniFab visuo-tactile sensor integrated into a comfortable wearable form factor. Furthermore, we introduce ForceVT, a multimodal representation learning framework that leverages force cues to guide tactile learning, thereby enhancing inference robustness across diverse tactile conditions. Experimental results demonstrate that AetheRock substantially improves data collection efficiency, while ForceVT effectively mitigates learning bottlenecks caused by variations in sensor fabrication and usage, significantly boosting the generalization capability of tactile perception.

Abstract

Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile and force sensors in handheld or wearable devices. To address these limitations, we first introduce AetheRock for gripper-force, vision, and tactile data collection, which is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip, a resistive pressure sensor at the human finger contact region, a customized PCB module, and a wearable kit for comfortable and robust collection. Building on this, we propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning, enabling robust inference in any tactile situation. Real-world experiments show that AetheRock achieves qualified data efficiency and that ForceVT effectively alleviates inefficiencies when visuo-tactile sensors exhibit manufacturing and utilization inconsistencies. Overall, our work mitigates the limitations of gripper-force vision-tactile robot learning through innovative hardware design and algorithms.

Problem

• force sensing
• tactile sensing
• wearable robotics
• sensor integration
• contact-rich manipulation

Innovation

• wearable robot teaching
• visuo-tactile sensing
• force-guided learning
• modular sensor design
• representation learning