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

2026-09-22

AI Summary

本文针对柔性线性物体（如电缆）的自动化操作难题，提出了一种结合双向长短期记忆网络学习和模型预测路径积分控制的方法。

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.

Problem

• Deformable Linear Objects
• automation
• infinite degrees of freedom
• non-linear dynamics

Innovation

• bidirectional Long Short-Term Memory
• Model Predictive Path Integral Control
• Deformable Linear Objects