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This paper introduces COIN, a framework that leverages LLMs to infer object ownership for service robots by integrating user background and object usage history. To address uncertainty in ownership inference, COIN employs conformal prediction to generate a set of plausible owners and selectively queries the user when the prediction is uncertain. Experiments in a simulated home environment demonstrate that COIN outperforms baselines, achieving a Subset Accuracy of 0.988 and a Mean Jaccard index of 0.991, even in scenarios with temporary use and shared ownership.
Service robots can now infer object ownership with near-perfect accuracy by intelligently combining LLM reasoning with targeted user questions only when uncertain.
Service robots must infer object ownership to correctly interpret instructions such as"bring me my cup."However, ownership is a latent attribute that cannot be directly observed, and existing methods often rely on limited cues such as recent usage, making them unreliable in scenarios such as temporary sharing. We propose a framework for context-aware ownership inference with uncertainty-guided interaction (COIN). The method integrates user background information and object usage history using a large language model (LLM) to estimate ownership scores. To handle uncertainty, we apply conformal prediction to construct a set of plausible owners and selectively generate user queries when the prediction is uncertain. Experiments in a simulated home environment show that the proposed method consistently outperforms baseline approaches, achieving a Subset Accuracy of 0.988 and a Mean Jaccard index of 0.991. The method also maintains high performance in scenarios involving temporary use and shared ownership. The results demonstrate that combining contextual reasoning with uncertainty-aware interaction improves both estimation accuracy and robustness. The project page is available at https://emergentsystemlabstudent.github.io/COIN/.
