论文/arXiv 2609.37681·29 天前

ProAct-VLM：基于连续感知反馈的故障前视觉-语言任务重规划

ProAct-VLM通过实时感知反馈将VLM嵌入任务规划，在动态长程操作任务中提前重规划并验证成功率与效率提升。

AI 整理

• 具身模型
• 端侧部署
• 评测与安全

原文链接：ProAct-VLM: Pre-Failure Vision-Language Task Replanning with Continuous Perception Feedback（arXiv 2609.37681 · 发布日期：2026-09-29）

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

ProAct-VLM通过实时感知反馈将VLM嵌入任务规划，在动态长程操作任务中提前重规划并验证成功率与效率提升。

核心要点

• 长程机器人任务易受环境突变影响，传统规则式管线脆弱且泛化不足。
• 框架将VLM置于实时感知—反馈循环，持续监测并在相关变化出现时重规划。
• 相较失败后或离散检查式重规划，它旨在执行失败前适应动态变化。
• 在动态长程操作任务中，与多个基线及不同VLM骨干比较，成功率和效率更高；提供项目页。

验证范围与局限：验证范围为摘要所述的动态长程操作任务，未说明仿真或真机设置。

英文摘要 (Abstract)

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: https://github.com/moured/ProAct-VLM

作者：Ahmed N. Ahmed, Omar Moured, Mughni Irfan Mohammed Abdul, Muhayy Ud Din, Irfan Hussain

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.37681：ProAct-VLM: Pre-Failure Vision-Language Task Replanning with Continuous Perception Feedback访问 2026-10-21