In partially observed environments, it can be useful for a human to provide
the robot with declarative information that represents probabilistic relational
constraints on properties of objects in the world, augmenting the robot's
sensory observations. For instance, a robot tasked with a search-and-rescue
mission may be informed by the human that two victims are probably in the same
room. An important question arises: how should we represent the robot's
internal knowledge so that this information is correctly processed and combined
with raw sensory information? In this paper, we provide an efficient belief
state representation that dynamically selects an appropriate factoring,
combining aspects of the belief when they are correlated through information
and separating them when they are not. This strategy works in open domains, in
which the set of possible objects is not known in advance, and provides
significant improvements in inference time over a static factoring, leading to
more efficient planning for complex partially observed tasks. We validate our
approach experimentally in two open-domain planning problems: a 2D discrete
gridworld task and a 3D continuous cooking task. A supplementary video can be
found at this http URL

该篇论文介绍了一个关于机器人在部分观察环境下进行可靠的推理与规划的有效方法，通过提供现实世界中物体属性的概率信息，并采取正确的策略，实现基于内部状态的自我学习，并验证了该方法在两个开放领域的规划问题中的有效性。