  In this paper, we present a planning system based on semantic reasoning for a
general-purpose service robot, which is aimed at behaving more intelligently in
domains that contain incomplete information, under-specified goals, and dynamic
changes. First, Two kinds of data are generated by Natural Language Processing
module from the speech: (i) action frames and their relationships; (ii) the
modifier used to indicate some property or characteristic of a variable in the
action frame. Next, the goals of the task are generated from these action
frames and modifiers. These goals are represented as AI symbols, combining
world state and domain knowledge, which are used to generate plans by an Answer
Set Programming solver. Finally, the actions of the plan are executed one by
one, and continuous sensing grounds useful information, which make the robot to
use contingent knowledge to adapt to dynamic changes and faults. For each
action in the plan, the planner gets its preconditions and effects from domain
knowledge, so during the execution of the task, the environmental changes,
especially those conflict with the actions, not only the action being
performed, but also the subsequent actions, can be detected and handled as
early as possible. A series of case studies are used to evaluate the system and
verify its ability to acquire knowledge through dialogue with users, solve
problems with the acquired causal knowledge, and plan for complex tasks
autonomously in the open world.
