基于事件驱动的主动辅助操作与 grounded 视觉语言规划
Event-Driven Proactive Assistive Manipulation with Grounded Vision-Language Planning
AI总结：
本文提出基于事件驱动的主动辅助操作方法，通过跟踪交互进度并分析状态转换，推断任务目标并生成辅助动作序列，提升人机协作效率。
摘要：
在协作操作中，帮助通常由用户指令触发，但流畅的人机合作中，伙伴常通过观察动作结果推断下一步行动而非等待指令。为此，本文引入事件驱动的主动辅助方法，机器人动作由人-物交互引发的工作空间状态转换触发。提出一个事件驱动框架，通过事件监控跟踪交互进度，并在事件完成时提取稳定前后快照以表征状态转换。基于稳定快照，规划器分析隐含的状态转换以推断任务目标并决定是否干预；若需干预，则生成一系列辅助动作。为使输出可执行和可验证，动作被限制为一组动作原始语和通过整数ID引用的对象。在真实的桌面数字块协作任务中评估该框架，证明显式的前后状态变化证据能提高可解场景的主动完成效率，并在不可解场景中适当等待。
英文摘要：
Assistance in collaborative manipulation is often initiated by user instructions, making high-level reasoning request-driven. In fluent human teamwork, however, partners often infer the next helpful step from the observed outcome of an action rather than waiting for instructions. Motivated by this, we introduce a shift from request-driven assistance to event-driven proactive assistance, where robot actions are initiated by workspace state transitions induced by human--object interactions rather than user-provided task instructions. To this end, we propose an event-driven framework that tracks interaction progress with an event monitor and, upon event completion, extracts stabilized pre/post snapshots that characterize the resulting state transition. Given the stabilized snapshots, the planner analyzes the implied state transition to infer a task-level goal and decide whether to intervene; if so, it generates a sequence of assistive actions. To make outputs executable and verifiable, we restrict actions to a set of action primitives and reference objects via integer IDs. We evaluate the framework on a real tabletop number-block collaboration task, demonstrating that explicit pre/post state-change evidence improves proactive completion on solvable scenes and appropriate waiting on unsolvable ones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
