  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.
