This dataset contains all trainingsdata and model weights which are used within the paper "Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short Term Memory Learning".

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.

The dataset contains the following files:

model weights

biLSTM_bs128_hs256_lr00001_epochs50_10k.pth biLSTM_bs128_hs256_lr00001_epochs50_20k.pth biLSTM_bs128_hs256_lr00001_epochs50_30k.pth

rollout dataset (rollout.npz) trainingdata

dataset_10k.npz dataset_20k.npz dataset_30k.npz

python file for extracting data from .npz files (getDataset.py)

Python, 3.12.3