Goal Tracking and Goal Attainment: A Natural Language Means of Achieving Adjustable Autonomy

Intelligent mobile robots that interact with humans must be able to exhibit adjustable autonomy, that is the ability to dynamically adjust the level of autonomy of an agent depending on the situation. When intelligent robots require close interactions with humans, they will require modes of communication that enhance the ability for humans to communicate naturally and that allow greater interaction. Our previous work examined the use of multiple modes of communication, specifically natural language and gestures, to disambiguate the communication between a human and a robot. In this paper, we propose using context predicates to keep track of various goals during human-robot interactions. These context predicates allow the robot to maintain multiple goals, each with possibly different levels of required autonomy. They permit direct human interruption of the robot, while allowing the robot to smoothly return to a high level of autonomy.