  Robot sequential decision-making in the real world is a challenge because it
requires the robots to simultaneously reason about the current world state and
dynamics, while planning actions to accomplish complex tasks. On the one hand,
declarative languages and reasoning algorithms well support representing and
reasoning with commonsense knowledge. But these algorithms are not good at
planning actions toward maximizing cumulative reward over a long, unspecified
horizon. On the other hand, probabilistic planning frameworks, such as Markov
decision processes (MDPs) and partially observable MDPs (POMDPs), well support
planning to achieve long-term goals under uncertainty. But they are
ill-equipped to represent or reason about knowledge that is not directly
related to actions.
  In this article, we present a novel algorithm, called iCORPP, to
simultaneously estimate the current world state, reason about world dynamics,
and construct task-oriented controllers. In this process, robot decision-making
problems are decomposed into two interdependent (smaller) subproblems that
focus on reasoning to "understand the world" and planning to "achieve the goal"
respectively. Contextual knowledge is represented in the reasoning component,
which makes the planning component epistemic and enables active information
gathering. The developed algorithm has been implemented and evaluated both in
simulation and on real robots using everyday service tasks, such as indoor
navigation, dialog management, and object delivery. Results show significant
improvements in scalability, efficiency, and adaptiveness, compared to
competitive baselines including handcrafted action policies.
