In this paper, we focus on inferring whether the given user command is clear,
ambiguous, or infeasible in the context of interactive robotic agents utilizing
large language models (LLMs). To tackle this problem, we first present an
uncertainty estimation method for LLMs to classify whether the command is
certain (i.e., clear) or not (i.e., ambiguous or infeasible). Once the command
is classified as uncertain, we further distinguish it between ambiguous or
infeasible commands leveraging LLMs with situational aware context in a
zero-shot manner. For ambiguous commands, we disambiguate the command by
interacting with users via question generation with LLMs. We believe that
proper recognition of the given commands could lead to a decrease in
malfunction and undesired actions of the robot, enhancing the reliability of
interactive robot agents. We present a dataset for robotic situational
awareness, consisting pair of high-level commands, scene descriptions, and
labels of command type (i.e., clear, ambiguous, or infeasible). We validate the
proposed method on the collected dataset, pick-and-place tabletop simulation.
Finally, we demonstrate the proposed approach in real-world human-robot
interaction experiments, i.e., handover scenarios.

本文研究了如何利用大型语言模型实现人机交互，并提出了一种基于不确定性估计方法的指令分类机制，利用上下文关系对摸棱两可的指令进行澄清，并使用问答生成技术与用户交互，最后在实际的人机交互场景中进行了验证。