日本フィジカルAI新聞

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arXiv:2311.09803

Learning effects in variable autonomy human-robot systems: how much training is enough?

Learning effects in variable autonomy human-robot systems: how much training is enough?

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著者: Manolis Chiou, Mohammed Talha, Rustam Stolkin

分類: cs.RO, cs.HC

原文アブストラクト

This paper investigates learning effects and human operator training practices in variable autonomy robotic systems. These factors are known to affect performance of a human-robot system and are frequently overlooked. We present the results from an experiment inspired by a search and rescue scenario in which operators remotely controlled a mobile robot with either Human-Initiative (HI) or Mixed-Initiative (MI) control. Evidence suggests learning in terms of primary navigation task and secondary (distractor) task performance. Further evidence is provided that MI and HI performance in a pure navigation task is equal. Lastly, guidelines are proposed for experimental design and operator training practices.