Newest papers

EventSync: Event-Driven Memory Updates for History-Dependent Manipulation

Jingjiao Zhao, Jing Dong, Mingyu Cao, Ruochuan Shi, Xiansheng Chen, Pengwei Wang, Zhiwu Lu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27917 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

历史依赖的机器人操纵需要任务背景和交互历史,因为当前的观察仅不足以确定下一步的行动。 层次性框架使用任务内存用于高层次的子目标推理和低层次的执行策略。 然而,内存更新需要准确的完成评估:类似的观察可能与不同的执行阶段相匹配,而视觉语言的完成模型可以误导后续的子目标推理。 我们介绍了EventSync,一个层次性框架,通过事件驱动的记忆更新协调任务推理和行动执行。 视觉语言模型会产生来自任务计划和交互历史的子目标,而视觉语言的完成模型会执行它们。 轻量级事件监测将视觉观察与终端效力轨迹结合起来,以确定当前的子目标内存

Machine translation (Meta M2M100); the English original follows.

Abstract

History-dependent robotic manipulation requires task context and interaction history, as the current observation alone is insufficient to determine the next action. Hierarchical frameworks use task memory for high-level subgoal reasoning and low-level policies for execution. However, memory updates require accurate completion assessment: similar observations may correspond to different execution phases, and prematurely recording completion can mislead subsequent subgoal reasoning. We present EventSync, a hierarchical framework that coordinates task reasoning and action execution through event-driven memory updates. A vision-language model generates subgoals from task plans and interaction history, while a vision-language-action model executes them. A lightweight Event Monitor combines visual observations with end-effector trajectories to determine whether the current subgoal is complete. The framework records the current subgoal in its completed-subgoal history and triggers subsequent reasoning only after obtaining completion evidence. With visual history in the executor, EventSync achieves 64.5% overall success on RoboMME, exceeding the best baseline reported in the benchmark by 20.0 percentage points. Further analysis shows that this update mechanism helps build a reliable completed-subgoal history. Experiments on four real-robot tasks with different memory requirements validate the framework's effectiveness in real-world robotic manipulation.

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Topics: Computer science, Task (project management), Context (archaeology), Artificial intelligence, Benchmark (surveying), Action (physics)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange