TanCRAFT：面向装配任务的上下文推理式预期反馈
TanCRAFT: Context-Reasoned Anticipatory Feedback for Assembly Tasks
Sassan Mokhtar, Lars Doorenbos, Marius Bock, Sina Mokhtar, Katharina Beckh, Dominik Bach, Juergen Gall
AI 速读 基于摘要
TanCRAFT 是一个四阶段实时预测式辅助系统，通过跟踪场景、推理当前部分装配、预测被操作部件的近期运动，在预测放置与目标不兼容时提前干预，并在七巧板装配任务中验证其效果。
cs.RO · cs.CV
查看原文摘要
Preventing human error in physical assembly tasks requires assistance systems that can intervene before mistakes are made. This is especially challenging in settings where users are free to choose the construction order and correctness depends on the evolving assembly rather than a fixed next step. We present TanCRAFT, a four-stage real-time predictive assistance system that tracks the scene state, reasons about the current partial assembly, forecasts the near-future motion of manipulated pieces, and intervenes when the predicted placement is incompatible with the target. We instantiate our framework and confirm its effectiveness in a controlled tangram assembly task through offline benchmarks and a within-subject user study. Our findings show that the predictive feedback significantly reduces mistakes relative to unguided and reactive sessions, while yielding the lowest mean perceived workload and shortest mean completion time. Overall, these results demonstrate the value of predictive feedback for error prevention and provide a step toward cost-aware assistance for automation and human-robot workcells.