An hyperreality imagination based reasoning and evaluation system (HIRES)

In this work we ask whether an integrated system based on the concept of human imagination and realized as a hyperreal setup can improve system robustness and autonomy. In particular we focus on how non-nominal failures in a planning-based system can be detected before actual failure. To investigate, we integrated a system combining an accurate physics-based simulation, robust object recognition and a symbolic planner to achieve realistic prediction of robot actions. A Gazebo simulation was used to reason about and evaluate situations before and during plan execution. The simulation enabled re-planning to take place in advance of actual plan failure. We present a restaurant scenario in which our system prevents plan failure and successfully lets the robot serve a drink on a table cluttered with objects. The results give us confidence in our approach to improving situations where unavoidable abstractions of robot action planning meet the real world.