AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
AetheRock: 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.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Dataset details reported by the source
- Modalities
- force and tactile sensing Open source S2
- Embodiments
- arm-worn device Open source S3
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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,
Open source S2
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
Open source S3
ltimodal 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 fidel
Open source S4
ity. Project page: https://lihongcs.github.io/aetherock.
Open source S5
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Confirm the required data type and collection setting with the buyer; this source does not establish a complete collection specification.
- Check the exact dataset version, license, modalities and task coverage against the intended use.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org