共享协作式遥操作框架，大幅提升精密装配数据采集效率与成功率。
SharedAssembly: A Data Collection Approach via Shared Tele-Assembly
- 通过双端智能协同，降低装配操作技能门槛。
- 亚毫米级装配任务成功率达97%，且间隙越小优势越明显。
- 新手可超越传统专家操作员，适合大规模接触式操作数据收集。
高精度、紧公差装配示范对于训练具备触觉感知能力的机器人基础模型至关重要，但传统遥操作方式因操作门槛高而难以获取。为此，我们提出SharedAssembly——一种嵌入装配特异性智能的共享自主双向遥操作框架，实现领导者与跟随者两端协同优化。在真实世界中对挑战性亚毫米级任务的用户研究表明，SharedAssembly实现了97%的装配成功率，显著提升完成效率。尤其当装配间隙减小时，性能提升更为显著。此外，该框架有效弥合了操作者经验差距，使新手操作员在传统系统下表现优于专家。通过降低技能门槛，SharedAssembly为接触密集型操作的大规模数据采集提供了高效、鲁棒且可及的解决方案。
原文摘要 · Abstract (English)
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.
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