Robotic task instructions often involve a referred object that the robot must
locate (ground) within the environment. While task intent understanding is an
essential part of natural language understanding, less effort is made to
resolve ambiguity that may arise while grounding the task. Existing works use
vision-based task grounding and ambiguity detection, suitable for a fixed view
and a static robot. However, the problem magnifies for a mobile robot, where
the ideal view is not known beforehand. Moreover, a single view may not be
sufficient to locate all the object instances in the given area, which leads to
inaccurate ambiguity detection. Human intervention is helpful only if the robot
can convey the kind of ambiguity it is facing. In this article, we present DoRO
(Disambiguation of Referred Object), a system that can help an embodied agent
to disambiguate the referred object by raising a suitable query whenever
required. Given an area where the intended object is, DoRO finds all the
instances of the object by aggregating observations from multiple views while
exploring & scanning the area. It then raises a suitable query using the
information from the grounded object instances. Experiments conducted with the
AI2Thor simulator show that DoRO not only detects the ambiguity more accurately
but also raises verbose queries with more accurate information from the
visual-language grounding.

DoRO 是一个可以提出适当查询帮助机器人消除歧义的系统，可以通过结合多个视图的观察来找到给定区域内对象的所有实例，并使用这些信息提出合适的查询。在 AI2Thor 模拟器上的实验表明，DoRO 不仅能更准确地检测到歧义，还可以使用来自视觉 - 语言基础的更准确信息发起详细查询。

DoRO: 针对具身代理的所指对象消歧

DoRO: Disambiguation of referred object for embodied agents

Anaphoric expressions, such as pronouns and referential descriptions, are
situated with respect to the linguistic context of prior turns, as well as, the
immediate visual environment. However, a speaker's referential descriptions do
not always uniquely identify the referent, leading to ambiguities in need of
resolution through subsequent clarificational exchanges. Thus, effective
Ambiguity Detection and Coreference Resolution are key to task success in
Conversational AI. In this paper, we present models for these two tasks as part
of the SIMMC 2.0 Challenge (Kottur et al. 2021). Specifically, we use TOD-BERT
and LXMERT based models, compare them to a number of baselines and provide
ablation experiments. Our results show that (1) language models are able to
exploit correlations in the data to detect ambiguity; and (2) unimodal
coreference resolution models can avoid the need for a vision component,
through the use of smart object representations.

本文研究了会话人工智能中的歧义检测和指代消解，使用 TOD-BERT 和 LXMERT 模型等对此进行了实现和对比，并得出了语言模型能够检测歧义且单模态指代消解模型可通过智能对象表示避免使用视觉组件等结论。