状态、机器人知识和人类（错误）信念的联合推理
Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs
摘要：
为了了解人类的（错误）信念-一个核心的社会认知能力-会影响人类与机器人的互动，本文提出了采用图形模型来统一表示对象的状态，机器人的知识，和人类（错误）信念。具体而言，解析图（PG）是从单视图时空解析通过聚合各种对象状态沿时间学习;这样的学习表示作为机器人的知识积累。推导了一种推理算法，将多个视角下所有机器人的个体轨迹融合成一个联合轨迹，从而提供了更有效的推理和推理能力，克服了单一视角下产生的错误。在实验中，通过对pg-s的联合推理，该系统正确地识别了各种设置中的人类（错误）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精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
