Programming robotic agents with action descriptions

Abstract

This paper tackles the problem of generalizing robot control programs over multiple objects, tasks and environments, based on the concept of action descriptions. These are abstract, general, semantic descriptions of an action that are augmented during execution with subsymbolic parameters specific to the context at hand. The parameters are inferred through reasoning rules, which extract the context from the action description and the belief state of the robot. The proposed system scales well with increasing number of reasoning rules required to support the knowledge-intensive manipulation tasks. The architecture combines the high-level robot control program with the reasoning engine in a modular way, thus improving the scalability of the system. The approach is validated in the context of setting a table with a PR2 robot.