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

• URL: http://arxiv.org/abs/2603.04705v1
• Date: Thu, 05 Mar 2026 01:04:23 GMT
• Title: LEGS-POMDP: Language and Gesture-Guided Object Search in Partially Observable Environments
• Abstract summary: LanguagE and Gesture-Guided Object Search in Partially Observable Environments (LEGS-POMDP)<n>We introduce LanguagE and Gesture-Guided Object Search in Partially Observable Environments (LEGS-POMDP), a modular POMDP system that integrates language, gesture, and visual observations for open-world object search.<n>In simulation, multimodal fusion significantly outperforms unimodal baselines, achieving an average success rate of 89% across challenging environments and object categories.
• Score: 7.458277072199384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 introduce LanguagE and Gesture-Guided Object Search in Partially Observable Environments (LEGS-POMDP), a modular POMDP system that integrates language, gesture, and visual observations for open-world object search. Unlike prior work, LEGS-POMDP explicitly models two sources of partial observability: uncertainty over the target object's identity and its spatial location. In simulation, multimodal fusion significantly outperforms unimodal baselines, achieving an average success rate of 89\% across challenging environments and object categories. Finally, we demonstrate the full system on a quadruped mobile manipulator, where real-world experiments qualitatively validate robust multimodal perception and uncertainty reduction under ambiguous instructions.

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