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DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation — arXiv:2609.27338

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27338
License (as archived): CC0-1.0
Archived: 2026-10-08T12:53:29.919Z
SHA-256 of the archived text: cbd97a0606e4ceb0b1dc3eb4175000f9ddb570ff107074009d433e1a4dac1e67
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How to cite

DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation — arXiv:2609.27338 — arXiv (en), https://arxiv.org/abs/2609.27338 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df4e981aa8cf (Metraipolis archive dc_df4e981aa8cf, sha256 cbd97a0606e4ceb0b1dc3eb4175000f9ddb570ff107074009d433e1a4dac1e67)

BibTeX

@misc{dcdf4e981aa8cf,
  title = {DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation — arXiv:2609.27338},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27338},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df4e981aa8cf; sha256 cbd97a0606e4ceb0b1dc3eb4175000f9ddb570ff107074009d433e1a4dac1e67},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27338. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may persist after nominal policy training. We present a risk-aware reinforcement-learning framework that addresses these two issues through an uncertainty-aware Dynamic Uncertainty Grid Map (DUGM) and a modular post-training recovery mechanism. DUGM combines local occupancy, estimated obstacle motion, and motion-estimation uncertainty in a robot-centric representation. After the nominal policy is frozen, a finite-horizon Risk Value Function (RVF) is trained from nominal rollouts and used to trigger a dedicated recovery policy when continued nominal execution is predicted to be collision-prone. Experiments in a held-out NVIDIA Isaac Sim clinical-logistics benchmark show that uncertainty-aware dynamic representation improves nominal navigation over static and deterministic alternatives, while the recovery mechanism further mitigates residual collision-prone behaviour. The complete framework is also deployed directly on a TurtleBot3 without policy fine-tuning, retraining, or site-specific adaptation, retaining the performance trend observed in simulation. These results indicate that uncertainty-aware dynamic representation and post-training recovery provide complementary mechanisms for improving learned local navigation.

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/2609.27338 — 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 2026-10-08T12:21:19.459Z 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).