Whose Is This?: Context-Aware Object Ownership Inference with Uncertainty-Guided Questioning

2026-05-27

AI Summary

Service robots must infer unobservable object ownership to accurately execute user instructions, yet existing approaches rely on limited cues—such as recent usage—and perform poorly in complex scenarios like temporary sharing. This work proposes a context-aware ownership inference method that integrates user profiles with object usage histories. It uniquely combines large language models with conformal prediction to construct a set of plausible ownership assignments and proactively initiates interactive clarification queries based on predictive uncertainty. Evaluated in simulated household environments, the approach achieves a subset accuracy of 0.988 and an average Jaccard index of 0.991, demonstrating high robustness even in challenging shared and transient-use settings.

Abstract

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/.

Problem

• object ownership
• service robots
• context-aware inference
• uncertainty
• human-robot interaction

Innovation

• ownership inference
• large language model
• conformal prediction
• uncertainty-guided questioning
• context-aware reasoning