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Reactive Task-Oriented Robot-Human Handovers via Generative Hypothesis Selection — arXiv:2610.08003

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.08003
License (as archived): CC0-1.0
Archived: 2026-10-07T13:12:13.378Z
SHA-256 of the archived text: f91da95a0de910be891f62b0e6f431c1097cb8d10a499d31d04a24d82d839be6
Stable URL of this page: https://metraipolis.com/documento/dc_adecb3eecf2b

How to cite

Reactive Task-Oriented Robot-Human Handovers via Generative Hypothesis Selection — arXiv:2610.08003 — arXiv (en), https://arxiv.org/abs/2610.08003 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_adecb3eecf2b (Metraipolis archive dc_adecb3eecf2b, sha256 f91da95a0de910be891f62b0e6f431c1097cb8d10a499d31d04a24d82d839be6)

BibTeX

@misc{dcadecb3eecf2b,
  title = {Reactive Task-Oriented Robot-Human Handovers via Generative Hypothesis Selection — arXiv:2610.08003},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.08003},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_adecb3eecf2b; sha256 f91da95a0de910be891f62b0e6f431c1097cb8d10a499d31d04a24d82d839be6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.08003. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

When humans hand each other objects, they incorporate both geometric and semantic information into this process. For example, passing a knife with the handle towards the recipient, rather than the blade, is both more ergonomic and safer. Recent state-of-the-art methods for task-oriented robot-human handovers have progressed from modeling object geometry to incorporating object affordances. However, they often forgo predicting the explicit, task-specific hand poses a human selects to utilize an object. Since many objects support multiple interaction modalities, e.g., a claw hammer used to strike or pull nails, this variability must be modeled to achieve robust task-oriented handovers. To tackle this, we propose a novel approach, GENESIS-Handover (GENErative HypotheSIS), which leverages VLM image generation to produce a variety of task-specific hand-object interaction hypotheses. These hypotheses are matched in real time to the observed human hand pose, enabling inference of the most suitable handover configuration. By leveraging VLMs as priors of plausible hand-object interactions, the method produces task-conditioned handover strategies for previously unseen object-task pairs. We evaluate the standalone interaction proposal module before deploying the full system on a mobile manipulator. In a user study with 12 participants across five task-object pairs, 83.3% perceived our method to have better task understanding than the previous state of the art.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.08003 — 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 2026-10-07T12:42:33.592Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).