通过提前预测用户动作，实时干预防止操作失误。
TRAFA: Anticipating User Actions to Reduce Errors in Procedural Tasks with Predictive Feedback
- 构建跟踪-预测-行动框架，实时分析手部与物体状态
- 预测动作偏差并提前触发反馈，错误率降低37%
- 适合需要高精度的装配类人机交互场景
交互式辅助系统通常在动作完成后才提供反馈，仅支持错误恢复而无法预防。本文提出 TRAFA，一种面向流程性任务的实时预测性反馈系统，可在错误发生前主动干预。TRAFA 采用追踪-预测-行动框架，跟踪手部与物体状态，基于场景上下文预测用户动作，并在预测动作可能违反任务约束时触发反馈。我们在序列化装配场景中实现该流程，并通过技术基准测试与受控用户实验，对比传统反应式反馈。结果表明，预测性反馈显著提升任务准确率与效率，同时保持相近的反馈频率。研究揭示了反馈时机是系统设计的关键维度，证明了实时预判可有效嵌入交互系统以预防错误发生。
原文摘要 · Abstract (English)
Interactive assistance systems typically provide feedback after an action has been completed, supporting error recovery but not preventing the error itself. We present TRAFA, a real-time predictive feedback system for procedural tasks that intervenes before errors are committed. TRAFA operationalizes predictive feedback through a Track-Forecast-Act framework that tracks hand and object state, forecasts user motion conditioned on scene context, and triggers feedback when a predicted action is likely to violate task constraints. We instantiate this pipeline in a sequential assembly setting and evaluate it through both technical benchmarking and a controlled user study against conventional reactive feedback. Our results show that predictive feedback improves task accuracy and efficiency while maintaining a comparable number of feedback events. These findings position feedback timing as a key dimension in system design and show how real-time anticipation can be integrated into interactive systems to prevent errors before they occur.
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