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AI agent learns to ask questions efficiently for object finding

Researchers have developed a new approach for instance goal navigation, where an embodied agent must find a specific object based on a vague description. The system treats interaction with an oracle as a cost-sensitive uncertainty-reduction problem, prompting the agent to ask questions that most efficiently resolve ambiguity. This work introduces a new benchmark and a Weighted Success Rate metric to evaluate interaction efficiency, moving beyond previous methods that did not account for varying question costs. AI

IMPACT This research could lead to more efficient embodied AI agents capable of understanding and acting on ambiguous instructions in complex environments.

RANK_REASON The cluster contains a research paper detailing a new method for instance goal navigation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xunyi Zhao, Sihao Lin, Gengze Zhou, Zerui Li, Shijie Li, Wei Tao, Jiajun Liu, Qi Wu ·

    Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation

    arXiv:2606.03175v1 Announce Type: new Abstract: Instance Goal Navigation (IGN) requires an embodied agent to find a specific object instance among distractors from an underspecified natural-language description. Such ambiguity often cannot be resolved from perception and language…