TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Mitrevski, Alex A1 - Plöger, Paul G. A1 - Lakemeyer, Gerhard T1 - A hybrid skill parameterisation model combining symbolic and subsymbolic elements for introspective robots JF - Robotics and Autonomous Systems N2 - In the design of robot skills, the focus generally lies on increasing the flexibility and reliability of the robot execution process; however, typical skill representations are not designed for analysing execution failures if they occur or for explicitly learning from failures. In this paper, we describe a learning-based hybrid representation for skill parameterisation called an execution model, which considers execution failures to be a natural part of the execution process. We then (i) demonstrate how execution contexts can be included in execution models, (ii) introduce a technique for generalising models between object categories by combining generalisation attempts performed by a robot with knowledge about object similarities represented in an ontology, and (iii) describe a procedure that uses an execution model for identifying a likely hypothesis of a parameterisation failure. The feasibility of the proposed methods is evaluated in multiple experiments performed with a physical robot in the context of handle grasping, object grasping, and object pulling. The experimental results suggest that execution models contribute towards avoiding execution failures, but also represent a first step towards more introspective robots that are able to analyse some of their execution failures in an explicit manner. KW - robot introspection KW - robot execution failures KW - skill execution models Y1 - 2023 SN - 0921-8890 SS - 0921-8890 U6 - https://doi.org/10.1016/j.robot.2022.104350 DO - https://doi.org/10.1016/j.robot.2022.104350 N1 - This article is part of the special issue on Semantic Policy and Action Representations for Autonomous Robots. N1 - Abstract provided by the author. VL - 161 SP - 22 S1 - 22 PB - Elsevier ER -