For a service robot, it is crucial to perceive as early as possible that an
approaching person intends to interact: in this case, it can proactively enact
friendly behaviors that lead to an improved user experience. We solve this
perception task with a sequence-to-sequence classifier of a potential user
intention to interact, which can be trained in a self-supervised way. Our main
contribution is a study of the benefit of features representing the person's
gaze in this context. Extensive experiments on a novel dataset show that the
inclusion of gaze cues significantly improves the classifier performance (AUROC
increases from 84.5% to 91.2%); the distance at which an accurate
classification can be achieved improves from 2.4 m to 3.2 m. We also quantify
the system's ability to adapt to new environments without external supervision.
Qualitative experiments show practical applications with a waiter robot.

对于服务机器人来说，关键是能够尽早地察觉到接近的人有意互动，以便主动采取友好行为，从而提升用户体验。我们通过一个自监督训练的序列到序列分类器来解决这个感知任务，主要研究了在这种情况下表示人的目光的特征的益处。在一个新的数据集上进行的大量实验证明，包含目光线索显著提高了分类器的性能（AUROC 从 84.5% 增加到 91.2%），能够实现准确分类的距离从 2.4 米提高到 3.2 米。我们还量化了系统在无外部监督的情况下适应新环境的能力。定性实验证明了与服务员机器人的实际应用。

预测与服务机器人互动意图：凝视线索的作用

Predicting the Intention to Interact with a Service Robot:the Role of  Gaze Cues

Humans are able to describe image contents with coarse to fine details as
they wish. However, most image captioning models are intention-agnostic which
can not generate diverse descriptions according to different user intentions
initiatively. In this work, we propose the Abstract Scene Graph (ASG) structure
to represent user intention in fine-grained level and control what and how
detailed the generated description should be. The ASG is a directed graph
consisting of three types of \textbf{abstract nodes} (object, attribute,
relationship) grounded in the image without any concrete semantic labels. Thus
it is easy to obtain either manually or automatically. From the ASG, we propose
a novel ASG2Caption model, which is able to recognise user intentions and
semantics in the graph, and therefore generate desired captions according to
the graph structure. Our model achieves better controllability conditioning on
ASGs than carefully designed baselines on both VisualGenome and MSCOCO
datasets. It also significantly improves the caption diversity via
automatically sampling diverse ASGs as control signals.

提出了一种基于 Abstract Scene Graph 结构的 ASG2Caption 模型，通过识别图形中的用户意图和语义，生成用户想要的注释，进而实现更好的可控性和注释多样性。