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MultiPush: Learning to Rearrange with Teams of Car-Like Pushers — arXiv:2609.27005

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27005
License (as archived): CC0-1.0
Archived: 2026-10-08T12:51:13.304Z
SHA-256 of the archived text: bbe431db1004c1608765361b8aafe2340a41460550880aeafded3d06cdb74283
Stable URL of this page: https://metraipolis.com/documento/dc_fab9f91c0398

How to cite

MultiPush: Learning to Rearrange with Teams of Car-Like Pushers — arXiv:2609.27005 — arXiv (en), https://arxiv.org/abs/2609.27005 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fab9f91c0398 (Metraipolis archive dc_fab9f91c0398, sha256 bbe431db1004c1608765361b8aafe2340a41460550880aeafded3d06cdb74283)

BibTeX

@misc{dcfab9f91c0398,
  title = {MultiPush: Learning to Rearrange with Teams of Car-Like Pushers — arXiv:2609.27005},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27005},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fab9f91c0398; sha256 bbe431db1004c1608765361b8aafe2340a41460550880aeafded3d06cdb74283},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27005. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We focus on the problem of rearranging multiple objects within a constrained workspace via pushing using a team of car-like robots. While the use of multiple robots offers the potential for more efficient execution, the need for conflict resolution and the kinematic constraints arising from physics, robot design, and the workspace boundary make this problem especially challenging. Our key insight is that by exploiting the structure introduced by the car-like kinematics of the domain, we could relax the problem into an ordered assignment of Dubins curves to robots. To this end, we introduce MultiPush, a reinforcement-learning based framework that jointly determines an efficient schedule of pushing tasks and their allocation to available robots by leveraging a constraint-aware traversability graph. Across extensive simulated trials with up to 14 objects and teams of two to four robots, MultiPush reduces the makespan by up to 16% compared to the baselines while requiring up to 2.9 times faster planning time. We demonstrate MultiPush on a real-world scenario involving the rearrangement of 12 objects by two and three robots (1/10-scale racecars) in a constrained space.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.27005 — 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 2026-10-08T12:21:19.423Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).