让机器人学人动作：用智能辅助构建带时间锚点的交互事件图
IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction
- 基于人类行为与证据质量，动态选择提问、建议或保守补全
- 减少13.5%人工操作，事件匹配率达46.67%，无确认字段错误
- 适合需高精度动作标注的机器人学习场景
我们提出IMPACT-HOI，一种混合智能框架，用于在第一人称流程视频中构建人类-物体交互（HOI）的结构化事件图，旨在为机器人从人类示范中学习操作提供高质量结构化监督。该任务被建模为逐步解析一个部分指定且以起始时刻为锚点的事件状态。一个信任校准的控制器根据标注者行为和证据质量，在直接提问、人类确认建议和保守补全之间进行选择。采用原子回滚的风险受限执行协议，确保人类确认决策不受后续自动更新干扰。9名参与者用户研究显示，人工标注操作减少13.5%，事件匹配率达46.67%，且在所研究协议下零确认字段违规。代码将公开于https://github.com/541741106/IMPACT_HOI。
原文摘要 · Abstract (English)
We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object Interactions (HOI), motivated by the need for high-quality structured supervision for learning robot manipulation from human demonstration. IMPACT-HOI frames this task as the incremental resolution of a partially specified, onset-anchored event state. A trust-calibrated controller selects among direct queries, human-confirmed suggestions, and conservative completions based on empirical annotator behavior and evidence quality. A risk-bounded execution protocol, utilizing atomic rollback, ensures that human-confirmed decisions are preserved against conflicting automated updates. A user study with 9 participants shows a 13.5% reduction in manual annotation actions, a 46.67% event match rate, and zero confirmed-field violations under the studied protocol. The code will be made publicly available at https://github.com/541741106/IMPACT_HOI.
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