# SharedAssembly: A Data Collection Approach via Shared Tele-Assembly

arXiv: https://arxiv.org/abs/2503.12287 · HTML: https://mlsanity.com/paper/2503.12287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2025.

## Built on by (most cited first, 1 of 1)

- [VT-Bridge: Bridging Pretrained Foundation VLAs to VTLAs via Lightweight Residual Adaptation](https://mlsanity.com/paper/2609.22606/) (2026)

## Abstract (arXiv)

High-precision, tight-clearance assembly demonstrations are indispensable for training tactile-aware robotic foundation models, yet their acquisition is heavily bottlenecked by the high operational barriers of conventional teleoperation. To bridge this gap, we propose SharedAssembly, a novel shared-autonomy bilateral teleoperation framework that embeds assembly-specific intelligence across both leader and follower sides to facilitate scalable data collection. Rigorous real-world user studies on challenging sub-millimeter tasks show that SharedAssembly achieves an exceptional 97% assembly success rate while significantly boosting completion efficiency. Notably, these performance gains become even more pronounced as the assembly clearance shrinks. Furthermore, our framework effectively eliminates the expertise gap, enabling novice operators to match or even outperform expert operators using conventional systems. By minimizing the skill barrier, SharedAssembly provides an efficient, robust, and accessible solution for large-scale data harvesting in contact-rich manipulation.