Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning

Abstract:The manipulation of Deformable Linear Objects (DLOs) such as cables poses a significant challenge for automation due to their infinite degrees of freedom and non-linear dynamics. In this paper we present a machine learning based optimal control approach for the manipulation of DLOs. This approach is divided into two main components: modeling and control. For modeling the dynamics of the DLO, we propose a learning based approach using a bidirectional Long Short-Term Memory (biLSTM) network. The biLSTM network is trained on synthetic data generated by the MuJoCo physics engine. For manipulating the DLO, a model predictive control strategy that employs Model Predictive Path Integral (MPPI) control is selected. The proposed approach is evaluated through simulation and experiments. The results demonstrate the effectiveness of the proposed method in achieving accurate and efficient manipulation of DLOs.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.26238 [cs.RO]

(or arXiv:2609.26238v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.26238 arXiv-issued DOI via DataCite (pending registration)

Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2025), Volume 1, pp. 47-58, SciTePress, 2025

https://doi.org/10.5220/0013703800003982 DOI(s) linking to related resources