Time-Series Data-Driven Three Dimensional Shape Control of Deformable Linear Objects Using a Dual-Arm Robot with Dynamic Model Updating

Abstract

Deformable objects(DOs) are prevalent in everyday environments and represent important targets for robotic manipulation. However, their high degrees of freedom and complex nonlinear deformations make them more challenging to model and control than rigid objects when relying on traditional analytical approaches. To address this, we propose a data-driven method to model the dynamics of deformable objects. Our method utilizes time-series data to predict future states without relying on complex dynamics. We employ model predictive control(MPC) for robot manipulation and improve its performance through online updates of the data-driven model. To handle cables with varying configurations, interpolation is applied to align model input structures. In this study, we focus on manipulating deformable linear objects(DLOs) with different mechanical properties and configurations using a dual-arm robotic system, both in simulation and in real-world environments.