Path signatures combined with dynamic time warping enable more accurate and efficient online goal recognition than prior state-of-the-art methods in continuous domains.
Journal of Artificial Intelligence Research , volume=
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SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.
citing papers explorer
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Online Goal Recognition using Path Signature and Dynamic Time Warping
Path signatures combined with dynamic time warping enable more accurate and efficient online goal recognition than prior state-of-the-art methods in continuous domains.
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SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models
SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.