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Fetch My Beer: Synthetic-to-real Hierarchical Policy for Smooth Pick-and-place — arXiv:2609.18119

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18119
License (as archived): CC0-1.0
Archived: 2026-10-15T16:55:45.279Z
SHA-256 of the archived text: e1a874955de42b8790ae48b40e5c140d7c31e6e71820c9e3e8bc82c2ebf98b1e
Stable URL of this page: https://metraipolis.com/documento/dc_ca4020fa140f

How to cite

Fetch My Beer: Synthetic-to-real Hierarchical Policy for Smooth Pick-and-place — arXiv:2609.18119 — arXiv (en), https://arxiv.org/abs/2609.18119 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ca4020fa140f (Metraipolis archive dc_ca4020fa140f, sha256 e1a874955de42b8790ae48b40e5c140d7c31e6e71820c9e3e8bc82c2ebf98b1e)

BibTeX

@misc{dcca4020fa140f,
  title = {Fetch My Beer: Synthetic-to-real Hierarchical Policy for Smooth Pick-and-place — arXiv:2609.18119},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18119},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ca4020fa140f; sha256 e1a874955de42b8790ae48b40e5c140d7c31e6e71820c9e3e8bc82c2ebf98b1e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18119. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Many real-world robotic applications require dynamically sensitive manipulation, where success depends not only on reaching a target state but on maintaining stable object dynamics throughout execution. We study the stable transport of liquid-filled containers, where a robot must move objects to target locations while suppressing sloshing and preventing spillage. Unlike conventional pick-and-place, this task imposes stringent requirements on motion smoothness and trajectory-level stability, exposing clear limitations in existing systems. Specifically, fluid simulation remains too costly for online reinforcement learning; human teleoperation introduces unintended accelerations that induce sloshing during imitation learning; and current policy pipelines optimize for task completion rather than dynamic stability. We propose a synthetic-to-real framework coupling physically validated data generation with a hierarchical, diffusion-based controller. The scalable data pipeline synthesizes grasps, filters unstable poses via a vision-language model, and validates transport trajectories through fluid simulation. The policy is organized with a high-level module that translates language and visual observations into SE(3) control targets, and a latent diffusion controller that first plans efficiently in a compact latent space and then decodes dense action chunks, enabling the high control frequency needed for smooth and stable motion. Extensive experiments show our system outperforms state-of-the-art manipulation policies in transport smoothness and dynamic stability. Our project page: this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.18119 — 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 2026-10-15T16:13:27.263Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).