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Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization — arXiv:2609.21211

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21211
License (as archived): CC0-1.0
Archived: 2026-10-10T02:30:03.067Z
SHA-256 of the archived text: 285d2569feb2d58f98b305a38f3433863b269eea46b47b45429f41d04319e2b3
Stable URL of this page: https://metraipolis.com/documento/dc_feb294cd2187

How to cite

Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization — arXiv:2609.21211 — arXiv (en), https://arxiv.org/abs/2609.21211 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_feb294cd2187 (Metraipolis archive dc_feb294cd2187, sha256 285d2569feb2d58f98b305a38f3433863b269eea46b47b45429f41d04319e2b3)

BibTeX

@misc{dcfeb294cd2187,
  title = {Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization — arXiv:2609.21211},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21211},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_feb294cd2187; sha256 285d2569feb2d58f98b305a38f3433863b269eea46b47b45429f41d04319e2b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21211. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Collision-free motion planning requires reliable collision models from sensed environments and validation of states along a continuous trajectory. To make this tractable, most planners check for collision at discrete states along continuous trajectories against a single determinized model of the environment, introducing a trade-off between safety and computational efficiency. While continuous collision checking approaches that approximate the swept volume of the robot exist, they are computationally expensive or overly conservative. Data-driven approaches can learn the swept volume; however, these neural models are susceptible to approximation errors and are therefore often limited to serving as coarse filters for downstream collision checkers. In this work, we propose to learn a signed distance function of the swept volume as a probabilistic field, enabling quantification of epistemic uncertainty, incorporation of perception noise, and eventual integration into a chance-constrained trajectory optimization framework. We demonstrate our approach on challenging high-dimensional manipulation problems with significant sensor noise, both in simulation and on real hardware.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-21 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.21211 — 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 2026-10-10T02:09:55.321Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).