The uncertainty and dynamic of home environment present great challenges to the task
planning of service robots. The nature of the home environment is highly unstructured, with a …

Reinforcement learning and symbolic planning have both been used to build intelligent
autonomous agents. Reinforcement learning relies on learning from interactions with real …

Robot planning in partially observable domains is difficult, because a robot needs to
estimate the current state and plan actions at the same time. When the domain includes …

This article describes REBA, a knowledge representation and reasoning architecture for
robots that is based on tightly-coupled transition diagrams of the domain at two different …

Abstract Reasoning with declarative knowledge (RDK) and sequential decision-making
(SDM) are two key research areas in artificial intelligence. RDK methods reason with …

Robots frequently need to perceive object attributes, such as red, heavy, and empty, using
multimodal exploratory behaviors, such as look, lift, and shake. One possible way for robots …

Reinforcement learning (RL) agents aim at learning by interacting with an environment, and
are not designed for representing or reasoning with declarative knowledge. Knowledge …

The main goal in task planning is to build a sequence of actions that takes an agent from an
initial state to a goal state. In robotics, this is particularly difficult because actions usually …

Robots who have partial observability of and incomplete knowledge about their
environments may have to consider contingencies while planning, and thus necessitate …

This paper presents a planning system based on semantic reasoning for a general-purpose
service robot, which is aimed at behaving more intelligently in domains that contain …