High-Level MLN-Based Approach for Spatial Context Disambiguation

Abstract

In this paper, we propose a probabilistic MLN-based model for spatial context disambiguation. This model serves as a solution for the problem of incomplete knowledge in High-level task planning. By applying the state of the art MLN probabilistic reasoning such as MCSAT, we determine the concept class of the current spatial context of the robot and contribute by combining semantic spatial relations with observed data at different timesteps. The inherent uncertainty of robot dynamic environments makes the proposed approach suitable to deal with partial observability and sensing limitations of robots. Simulation experiments and evaluation results are presented to validate our model.