Behavioral Cloning for Robotic Connector Assembly: An Empirical Study

TL;DR

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.

Abstract

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 %.

Figures (4)

• Figure 1: BC predictor based on human demonstrations. Force–torque controller targets are generated using a space mouse and a NN is trained to imitate insertion strategies from camera and force–torque sensor data.
• Figure 2: Hardware setup used in the experiments.
• Figure 3: Mean success rate over different history lengths $k$ and prediction horizons $h$ on a shared $x$-axis. In blue different $k$ given $h=1$. In green different $h$ given $k=10$.
• Figure 4: Mean success rate given a number of demonstrations $m$ with a comparison of a rule based stride search strategy from ERF_2025.