When to Act: Calibrated Confidence for Reliable Human Intention Prediction in Assistive Robotics
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914241186889728 |
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| author | Gaus, Johannes A. Ilg, Winfried Haeufle, Daniel |
| author_facet | Gaus, Johannes A. Ilg, Winfried Haeufle, Daniel |
| contents | Assistive devices must determine both what a user intends to do and how reliable that prediction is before providing support. We introduce a safety-critical triggering framework based on calibrated probabilities for multimodal next-action prediction in Activities of Daily Living. Raw model confidence often fails to reflect true correctness, posing a safety risk. Post-hoc calibration aligns predicted confidence with empirical reliability and reduces miscalibration by about an order of magnitude without affecting accuracy. The calibrated confidence drives a simple ACT/HOLD rule that acts only when reliability is high and withholds assistance otherwise. This turns the confidence threshold into a quantitative safety parameter for assisted actions and enables verifiable behavior in an assistive control loop. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04982 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | When to Act: Calibrated Confidence for Reliable Human Intention Prediction in Assistive Robotics Gaus, Johannes A. Ilg, Winfried Haeufle, Daniel Robotics Artificial Intelligence Assistive devices must determine both what a user intends to do and how reliable that prediction is before providing support. We introduce a safety-critical triggering framework based on calibrated probabilities for multimodal next-action prediction in Activities of Daily Living. Raw model confidence often fails to reflect true correctness, posing a safety risk. Post-hoc calibration aligns predicted confidence with empirical reliability and reduces miscalibration by about an order of magnitude without affecting accuracy. The calibrated confidence drives a simple ACT/HOLD rule that acts only when reliability is high and withholds assistance otherwise. This turns the confidence threshold into a quantitative safety parameter for assisted actions and enables verifiable behavior in an assistive control loop. |
| title | When to Act: Calibrated Confidence for Reliable Human Intention Prediction in Assistive Robotics |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2601.04982 |