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<p>Robots operating in domestic and office environments must understand object ownership to correctly execute instructions such as “Bring me my cup.” However, ownership cannot be reliably inferred from visual features alone. To address this gap, we propose Active Ownership Learning (ActOwL), a framework that enables robots to actively generate and ask users ownership-related questions. ActOwL employs a probabilistic generative model to select questions that maximize information gain, thereby acquiring ownership knowledge to improve learning efficiency. Additionally, by leveraging commonsense knowledge from large language models (LLMs), objects are pre-classified as either shared or owned, and only owned objects are targeted for questioning. Through experiments in a simulated home environment and a real-world laboratory setting, ActOwL achieved significantly higher ownership clustering accuracy with fewer questions than baseline methods. These findings demonstrate the effectiveness of combining active inference with LLM-guided commonsense reasoning, advancing robots’ ability to acquire ownership knowledge for practical, socially appropriate task execution. The project page is available at <a href="https://hashimoto-saki.github.io/ActOwl/">https://hashimoto-saki.github.io/ActOwl/</a>.</p>

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Toward ownership understanding of objects: active question generation with large language model and probabilistic generative model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

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  • Saki Hashimoto,
  • Shoichi Hasegawa,
  • Tomochika Ishikawa,
  • Akira Taniguchi,
  • Yoshinobu Hagiwara,
  • Lotfi El Hafi,
  • Tadahiro Taniguchi

摘要

Robots operating in domestic and office environments must understand object ownership to correctly execute instructions such as “Bring me my cup.” However, ownership cannot be reliably inferred from visual features alone. To address this gap, we propose Active Ownership Learning (ActOwL), a framework that enables robots to actively generate and ask users ownership-related questions. ActOwL employs a probabilistic generative model to select questions that maximize information gain, thereby acquiring ownership knowledge to improve learning efficiency. Additionally, by leveraging commonsense knowledge from large language models (LLMs), objects are pre-classified as either shared or owned, and only owned objects are targeted for questioning. Through experiments in a simulated home environment and a real-world laboratory setting, ActOwL achieved significantly higher ownership clustering accuracy with fewer questions than baseline methods. These findings demonstrate the effectiveness of combining active inference with LLM-guided commonsense reasoning, advancing robots’ ability to acquire ownership knowledge for practical, socially appropriate task execution. The project page is available at https://hashimoto-saki.github.io/ActOwl/.