不確実性に頑健なロボットアシスタントのための能動的意図認識
Uncertainty-Resilient Active Intention Recognition for Robotic Assistants
人間の意図認識に伴う不確実性や知覚誤差に対処するため、意図認識POMDPを中心にプランナを組み合わせたフレームワークを提案し、実機ロボットで検証した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Juan Carlos Saborío, Marc Vinci, Oscar Lima, Sebastian Stock, Lennart Niecksch, Martin Günther, Alexander Sung, Joachim Hertzberg, Martin Atzmüller
分類: cs.RO, cs.AI
原文アブストラクト
Purposeful behavior in robotic assistants requires the integration of multiple components and technological advances. Often, the problem is reduced to recognizing explicit prompts, which limits autonomy, or is oversimplified through assumptions such as near-perfect information. We argue that a critical gap remains unaddressed -- specifically, the challenge of reasoning about the uncertain outcomes and perception errors inherent to human intention recognition. In response, we present a framework designed to be resilient to uncertainty and sensor noise, integrating real-time sensor data with a combination of planners. Centered around an intention-recognition POMDP, our approach addresses cooperative planning and acting under uncertainty. Our integrated framework has been successfully tested on a physical robot with promising results.
