论文标题
国家的共同推断，机器人知识和人类（错误）信念
Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs
论文作者
论文摘要
旨在了解人类（虚假）信念（一种核心的社会认知能力）将如何影响人类与机器人的互动，本文提议采用图形模型来统一对象状态，机器人知识和人类（虚假）信念的表示。具体而言，通过在当时汇总各种对象态来从单视图时空解析中学到解析图（PG）。这种学识渊博的表示形式被积累为机器人的知识。推论算法被得出从跨多视图中的所有机器人融合单个PG中的关节PG，这具有更有效的推理和推理能力，以克服源自单个视图的错误。在实验中，通过对PG-S的联合推断，该系统正确地识别了人类（错误）对各种环境的信念，并在具有挑战性的小对象跟踪数据集上实现了更好的跨视图准确性。
Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs. Specifically, a parse graph (pg) is learned from a single-view spatiotemporal parsing by aggregating various object states along the time; such a learned representation is accumulated as the robot's knowledge. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning and inference capability to overcome the errors originated from a single view. In the experiments, through the joint inference over pg-s, the system correctly recognizes human (false-)belief in various settings and achieves better cross-view accuracy on a challenging small object tracking dataset.
