用人类示范教机器人精准插入变形连接器，成功率超90%。
Behavioral Cloning for Robotic Connector Assembly: An Empirical Study
- 通过模仿人类操作，融合视觉与力觉信息预测插入动作。
- 在5种不同连接器上实现超过90%的插入成功率。
- 适合需要高精度柔性装配的工业场景，如汽车与航空制造。
自动化线束装配在汽车、电气柜和飞机生产中极具挑战性，主要源于电缆的可变形性及连接器几何形状的高度差异。此外，连接器需以有限力度插入，避免损坏，而其姿态变化显著。尽管人类能结合视觉与触觉反馈直观完成该任务，但编程工业机器人以适应性方式执行仍困难重重。本研究通过实证分析，探讨行为克隆在学习连接器插入动作预测模型中的适用性，该模型融合了力矩传感与固定位置相机信息。基于多达300次通过遥控UR5e机械臂配合SpaceMouse收集的人类成功示范数据，比较了多种网络架构及其他设计选择。最终系统在五种不同连接器几何形状下，于不同姿态条件下评估，整体插入成功率超过90%。
原文摘要 · Abstract (English)
Automating the assembly of wire harnesses is challenging in automotive, electrical cabinet, and aircraft production, particularly due to deformable cables and a high variance in connector geometries. In addition, connectors must be inserted with limited force to avoid damage, while their poses can vary significantly. While humans can do this task intuitively by combining visual and haptic feedback, programming an industrial robot for such a task in an adaptable manner remains difficult. This work presents an empirical study investigating the suitability of behavioral cloning for learning an action prediction model for connector insertion that fuses force-torque sensing with a fixed position camera. We compare several network architectures and other design choices using a dataset of up to 300 successful human demonstrations collected via teleoperation of a UR5e robot with a SpaceMouse under varying connector poses. The resulting system is then evaluated against five different connector geometries under varying connector poses, achieving an overall insertion success rate of over 90 %.
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