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

arXiv:2609.26238 · cs.RO, cs.SY, eess.SY · Submitted 2026-09-22 · Read on arXiv

cs.RO, cs.SY, eess.SY

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 12 pages, 10 figures, 3 tables, 22nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2025)

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

DOI: 10.5220/0013703800003982

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: 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.

Terminology

Model Predictive Path Integral — 11× in this paper
Model Predictive Control — 9× in this paper
Model Predictive Path Integral (MPPI) — 5× in this paper · explained in 3 episodes
Accuracy — 4× in this paper · explained in 1 episode
International Conference on Robotics and Automation — 3× in this paper
Model Predictive Control (MPC) — 3× in this paper · explained in 5 episodes
GNNs — 2× in this paper · explained in 2 episodes
Generalization — 2× in this paper · explained in 13 episodes
Loss Function — 2× in this paper · explained in 2 episodes
Adam — 1× in this paper · explained in 2 episodes
Adaptive Sampling — 1× in this paper · explained in 1 episode
Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
Mean Squared Error — 1× in this paper
Point Clouds — 1× in this paper · explained in 2 episodes
Reinforcement Learning — 1× in this paper · explained in 4 episodes
Robustness — 1× in this paper · explained in 5 episodes

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.

Terminology

Abstract

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