Using Probabilistic Context Awareness in a Deliberative Planner System

When a Social Robot is deployed in a service environment it has to manage a highly dynamic scenarios that provide a set of unknown circumstances: objects in different places and humans walking around. These conditions are challenging for an autonomous robot that needs to accomplish assistive tasks. These partially known scenarios has negative effects on hybrid architectures with deliberative planning systems adding extra sub-tasks to main goal or continuous re-planing with deadlocks. This paper proposes the use of a probabilistic Context Awareness System that provides a set of belief states of the environment to a symbolic planner enabling PDDL metrics. The Context Awareness System is composed by a Deep Learning classifier to process audio input from the environment, and an inference probabilistic module for generating symbolic knowledge. This approach delivers a method to generate correct plans efficiently. The solution presented in this paper is being successfully applied in a robot running Robot Operating System (ROS) on two experimental scenarios that illustrates the utility of the technique showing a reduction on execution time.