AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
Overview
This paper introduces AetheRock, an arm-worn device designed to collect gripper-force, visual, and tactile data using a low-cost GelSlim-Mini sensor at the fingertip and a resistive pressure sensor at the finger contact area. The authors propose ForceVT, a framework that aligns multimodal representations to guide tactile learning and mitigate sensor degradation effects. Real-world experiments show that AetheRock enables efficient data collection, with ForceVT improving average performance by 13% over vision-tactile baselines and maintaining an average task progress of 55.0% under reduced tactile fidelity compared to 58.0% at full fidelity.
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Dataset · modalities: force and tactile sensing
“arXiv:2606.09777v3 Announce Type: replace Abstract: Force and tactile sensing are essential for contact-rich manipulation, but their integration into handheld or wearable devices is constrained by cost, size, and mechanical compatibility. Tactile signals are also sensitive to manufacturing variations and wear during use, posing further challenges to policy learning. To address these hardware and learning challenges,”
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Dataset · embodiments: arm-worn device
“we introduce AetheRock, an arm-worn device for collecting gripper-force, visual, and tactile data. AetheRock incorporates GelSlim-Mini, our improved version of GelSlim 4.0 designed for low-cost, easy manufacturing, at the gripper fingertip, together with a resistive pressure sensor at the human finger contact area. To learn robust representations from the collected data, we propose ForceVT, a framework that aligns mu”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
This paper introduces AetheRock, an arm-worn device designed to collect gripper-force, visual, and tactile data using a low-cost GelSlim-Mini sensor at the fingertip and a resistive pressure sensor at the finger contact area. The authors propose ForceVT, a framework that aligns multimodal representations to guide tactile learning and mitigate sensor degradation effects. Real-world experiments show that AetheRock enables efficient data collection, with ForceVT improving average performance by 13% over vision-tactile baselines and maintaining an average task progress of 55.0% under reduced tactile fidelity compared to 58.0% at full fidelity.
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