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ProAct-VLM: Pre-Failure Vision-Language Task Replanning with Continuous Perception Feedback — arXiv:2609.37681

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37681
License (as archived): CC0-1.0
Archived: 2026-10-07T19:15:43.255Z
SHA-256 of the archived text: 7c8962a3fe060c561589fcba0c4f0c3151dabb261d3b54a4ea4256ad276d45a5
Stable URL of this page: https://metraipolis.com/documento/dc_fb447acdb406

How to cite

ProAct-VLM: Pre-Failure Vision-Language Task Replanning with Continuous Perception Feedback — arXiv:2609.37681 — arXiv (en), https://arxiv.org/abs/2609.37681 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fb447acdb406 (Metraipolis archive dc_fb447acdb406, sha256 7c8962a3fe060c561589fcba0c4f0c3151dabb261d3b54a4ea4256ad276d45a5)

BibTeX

@misc{dcfb447acdb406,
  title = {ProAct-VLM: Pre-Failure Vision-Language Task Replanning with Continuous Perception Feedback — arXiv:2609.37681},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37681},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fb447acdb406; sha256 7c8962a3fe060c561589fcba0c4f0c3151dabb261d3b54a4ea4256ad276d45a5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37681. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Long-horizon robotic tasks are vulnerable to unexpected environmental changes that can render planned actions ineffective or unsafe. To address this, robots must detect such changes as they occur, interpret their impact, and adjust their actions accordingly. Traditional rule-based decision-making pipelines are brittle in open-world conditions, as they are hand-tuned for specific scenarios and lack generalization. Vision-Language Models (VLMs) offer a promising alternative as they combine broad world knowledge with unified visual--text reasoning, enabling them to generalize across diverse scenarios and generate accurate, grounded task plans. However, for effective deployment in dynamic real-world settings, VLMs must be embedded into frameworks capable of handling uncertainty and environmental changes. Existing frameworks broadly address this reactively, triggering replanning only after execution failures or post-task checks, risking failed actions. Some methods verify conditions before actions, but these discrete checks miss changes occurring during execution. To address this, we present ProAct-VLM, an adaptive, physically grounded task planning framework that integrates VLMs within a real-time perception--feedback loop. ProAct-VLM continuously monitors the environment and re-plans as soon as relevant changes are detected, enabling adaptation before failure occurs. Evaluations against multiple baselines and across different VLM backbones show that our framework improves both success rates and efficiency in dynamic, long-horizon manipulation tasks. Project page: this https URL

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.37681 — 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 2026-10-07T18:40:59.529Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).