Assembly is a fundamental skill for robots in both modern manufacturing and service robotics. Existing datasets aim to address the data bottleneck in training general-purpose robot models, falling short of capturing contact-rich assembly tasks. To bridge this gap, we introduce SharedAssembly, a novel bilateral teleoperation approach with shared autonomy for scalable assembly execution and data collection. User studies demonstrate that the proposed approach enhances both success rates and efficiency, achieving a 97.0% success rate across various sub-millimeter-level assembly tasks. Notably, novice and intermediate users achieve performance comparable to experts using baseline teleoperation methods, significantly enhancing large-scale data collection.
@article{arxiv.2503.12287,
title = {SharedAssembly: A Data Collection Approach via Shared Tele-Assembly},
author = {Yansong Wu and Xiao Chen and Yu Chen and Hamid Sadeghian and Fan Wu and Zhenshan Bing and Sami Haddadin and Alexander König and Alois Knoll},
journal= {arXiv preprint arXiv:2503.12287},
year = {2025}
}