No abstract

This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.

No abstract

Abstract-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.

No abstract