外观
论文arXiv 2610.2121113 天前
面向主动机器人的基于 Procedural Assistance Memory 的智能体式框架
原文链接：An Agentic Framework for Proactive Robots with Procedural Assistance Memory（arXiv 2610.21211 · 发布日期：2026-10-15）
An Agentic Framework for Proactive Robots with Procedural Assistance Memory
提出基于 PAM 的智能体框架，用 VLM 预测任务、VLA 执行，在5天桌面辅助评估中验证主动协助。
核心要点
- 相同行为在不同用户下可能对应不同需求，常识推断易带来不必要协助。
- PAM 以活动阶段有限状态机、逐状态协助规则和事件驱动转移组织记忆。
- VLM 任务预测器读取 PAM 与当前观察，VLA 执行器完成预测任务。
- 5天评估中系统随经验提升，精确率、召回率和 F1 最高，并缩短首次触发延迟。
验证范围与局限：验证范围为5天桌面辅助评估中的主动任务与用户日常变化。
英文摘要 (Abstract)
Proactive robots should anticipate user needs and provide assistance before being explicitly asked. A straightforward approach is to infer human intentions from commonsense knowledge, but the same observed behavior can imply different needs across users, potentially leading to unwanted assistance. We observe that everyday routines in the real world exhibit recurring patterns, so learning these patterns can reduce ambiguity in proactive decisions. Based on this, we propose a memory-based agentic framework in which a VLM task predictor reads a Procedural Assistance Memory (PAM) alongside the current observation to decide when to assist and what task to perform, and a VLA executor performs the predicted task. PAM is structured as a finite-state machine over activity phases, per-state assistance rules, and event-driven transitions; a reflection VLM revises PAM from each day's interaction trace without weight updates or retraining. In our 5-day desk-assistance evaluation, memory-free baselines remain conservative, with limited recall on proactive tasks. Our system improves consistently with accumulated experience, achieving the highest precision, recall, and F1 and reducing first-fire delay from approximately 5 seconds on Day 1 to 1 second by Day 5. Our approach also adapts to changes in the user's routine through continued memory reflection, recovering from memory--routine misalignment after a single memory update. The project page is available at https://davian-robotics.github.io/PAM/
作者：Dohyun Lee, Minho Park, 박정훈, Dongyoon Hwang, Junha Hyung, Sungwon Hwang, Byungkun Lee, Jaegul Choo
来源
本页事实对应的公开页面，以原文为准。