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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.24526 |
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| _version_ | 1866916042227318784 |
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| author | Mokhtar, Sassan Doorenbos, Lars Jabbari, Fatemeh Bock, Marius Bach, Dominik Gall, Juergen |
| author_facet | Mokhtar, Sassan Doorenbos, Lars Jabbari, Fatemeh Bock, Marius Bach, Dominik Gall, Juergen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24526 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TRAFA: Anticipating User Actions to Reduce Errors in Procedural Tasks with Predictive Feedback Mokhtar, Sassan Doorenbos, Lars Jabbari, Fatemeh Bock, Marius Bach, Dominik Gall, Juergen Human-Computer Interaction Artificial Intelligence 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. |
| title | TRAFA: Anticipating User Actions to Reduce Errors in Procedural Tasks with Predictive Feedback |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2605.24526 |