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Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning — arXiv:2609.26238

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.26238
License (as archived): CC0-1.0
Archived: 2026-10-08T16:43:51.653Z
SHA-256 of the archived text: a257409c41a037b605604321140bd916982800830360bad73116849fe24468c5
Stable URL of this page: https://metraipolis.com/documento/dc_dc757ec99216

How to cite

Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning — arXiv:2609.26238 — arXiv (en), https://arxiv.org/abs/2609.26238 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dc757ec99216 (Metraipolis archive dc_dc757ec99216, sha256 a257409c41a037b605604321140bd916982800830360bad73116849fe24468c5)

BibTeX

@misc{dcdc757ec99216,
  title = {Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning — arXiv:2609.26238},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.26238},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dc757ec99216; sha256 a257409c41a037b605604321140bd916982800830360bad73116849fe24468c5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.26238. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-23 in cs.RO).
Categorie arXiv: Robotics (cs.RO) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.26238 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T15:56:53.488Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).