SharedAssembly：共有テレアセンブリによるデータ収集手法
SharedAssembly: A Data Collection Approach via Shared Tele-Assembly
リーダー側とフォロワー側の両方に組立特化の知能を組み込んだ共有自律バイラテラル遠隔操作フレームワークを提案し、サブミリ単位の高精度組立タスクで97%の成功率を達成した。
著者: Yansong Wu, Xiao Chen, Yu Chen, Hamid Sadeghian, Fan Wu, Zhenshan Bing, Alois Knoll
分類: cs.RO
原文アブストラクト
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.
