Uncertainty-Resilient Active Intention Recognition for Robotic Assistants
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911123066847232 |
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| author | Saborío, Juan Carlos Vinci, Marc Lima, Oscar Stock, Sebastian Niecksch, Lennart Günther, Martin Sung, Alexander Hertzberg, Joachim Atzmüller, Martin |
| author_facet | Saborío, Juan Carlos Vinci, Marc Lima, Oscar Stock, Sebastian Niecksch, Lennart Günther, Martin Sung, Alexander Hertzberg, Joachim Atzmüller, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncertainty-Resilient Active Intention Recognition for Robotic Assistants Saborío, Juan Carlos Vinci, Marc Lima, Oscar Stock, Sebastian Niecksch, Lennart Günther, Martin Sung, Alexander Hertzberg, Joachim Atzmüller, Martin Robotics Artificial Intelligence 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. |
| title | Uncertainty-Resilient Active Intention Recognition for Robotic Assistants |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2508.19150 |