LEGS-POMDP: Language and Gesture-Guided Object Search in Partially Observable Environments

Description

To assist humans in open-world environments, robots must interpret ambiguous instructions to locate desired objects. Foundation model-based approaches excel at multimodal grounding, but they lack a principled mechanism for modeling uncertainty in long-horizon tasks. In contrast, Partially Observable Markov Decision Processes (POMDPs) provide a systematic framework for planning under uncertainty but are often limited in supported modalities and rely on restrictive environment assumptions. We intr

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