Ontology-Assisted Generalisation of Robot Action Execution Knowledge
This addresses the challenge for autonomous robots in efficiently adapting to varied objects and contexts, though it is incremental as it builds on existing ontology-based approaches.
The paper tackles the problem of generalizing robot action execution policies to new objects using an object ontology, enabling the robot to transfer known models to related object classes and identify when additional learning is needed. The method was verified for grasping and stowing actions, showing it can deduce cases for generalization or acquisition of new knowledge.
When an autonomous robot learns how to execute actions, it is of interest to know if and when the execution policy can be generalised to variations of the learning scenarios. This can inform the robot about the necessity of additional learning, as using incomplete or unsuitable policies can lead to execution failures. Generalisation is particularly relevant when a robot has to deal with a large variety of objects and in different contexts. In this paper, we propose and analyse a strategy for generalising parameterised execution models of manipulation actions over different objects based on an object ontology. In particular, a robot transfers a known execution model to objects of related classes according to the ontology, but only if there is no other evidence that the model may be unsuitable. This allows using ontological knowledge as prior information that is then refined by the robot's own experiences. We verify our algorithm for two actions - grasping and stowing everyday objects - such that we show that the robot can deduce cases in which an existing policy can generalise to other objects and when additional execution knowledge has to be acquired.