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BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation — arXiv:2611.03059

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03059
License (as archived): CC0-1.0
Archived: 2026-11-03T11:44:48.043Z
SHA-256 of the archived text: 722b8adcc63eda238c6bc3e3601c11ff54aafd1d4198db72866367500d2330de
Stable URL of this page: https://metraipolis.com/documento/dc_abc281b9ef2a

How to cite

BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation — arXiv:2611.03059 — arXiv (en), https://arxiv.org/abs/2611.03059 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abc281b9ef2a (Metraipolis archive dc_abc281b9ef2a, sha256 722b8adcc63eda238c6bc3e3601c11ff54aafd1d4198db72866367500d2330de)

BibTeX

@misc{dcabc281b9ef2a,
  title = {BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation — arXiv:2611.03059},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03059},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abc281b9ef2a; sha256 722b8adcc63eda238c6bc3e3601c11ff54aafd1d4198db72866367500d2330de},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03059. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Contact-based robotic manipulation is constrained by end-effector reachability and the mechanical properties of objects. Airflow can exert forces without direct robot-object contact, enabling manipulation of objects beyond the reach or when direct contact is undesirable. However, the lack of suitable simulation has hindered the application of reinforcement learning to airflow-based manipulation, limiting the broader adoption of this mechanism compared with contact-based methods. To address this gap, we developed a robot- and task-agnostic airflow extension for Isaac Lab that supports massively parallel training. The extension uses experimentally identified airflow models to compute the forces and torques exerted on objects. Based on this extension, we train a hybrid push-blow (HPB) policy that combines contact-based pushing with airflow manipulation and a rectangular reorientation (RR) policy that controls a cuboid's heading using airflow alone. To enable physical execution, we design a lightweight, high-speed blower that coexists with a gripper. In zero-shot real-world deployment, HPB achieves an 80% success rate over 20 trials, while RR achieves a mean absolute heading error of 2.6 degrees across four target headings. The project materials are available at this https URL .

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2611.03059 — 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 2026-11-03T11:06:52.307Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).