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, 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 multimodal representations and uses force and vision to guide tactile learning, mitigating the effects of manufacturing variations and sensor degradation. Real-world experiments demonstrate efficient data collection with AetheRock. ForceVT improves average performance by 13% over the vision-tactile baseline and maintains an average task progress of 55.0% under reduced tactile fidelity, compared with 58.0% at full fidelity. Project page: https://lihongcs.github.io/aetherock.

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