2021/07/20 by Alex Mitrevski, Mitrevski, Alex, Paul G. Plöger +3 · 1 citation
Computer Science · #FOS: Computer and information sciences #Natural Language Processing Techniques #Reinforcement Learning in Robotics #Robotics (cs.RO) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2107.09353
openalex publication_date 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.