Volltext-Downloads (blau) und Frontdoor-Views (grau)

Representation and Experience-Based Learning of Explainable Models for Robot Action Execution

  • For robots acting in human-centered environments, the ability to improve based on experience is essential for reliable and adaptive operation; however, particularly in the context of robot failure analysis, experience-based improvement is only useful if robots are also able to reason about and explain the decisions they make during execution. In this paper, we describe and analyse a representation of execution-specific knowledge that combines (i) a relational model in the form of qualitative attributes that describe the conditions under which actions can be executed successfully and (ii) a continuous model in the form of a Gaussian process that can be used for generating parameters for action execution, but also for evaluating the expected execution success given a particular action parameterisation. The proposed representation is based on prior, modelled knowledge about actions and is combined with a learning process that is supervised by a teacher. We analyse the benefits of this representation in the context of two actions – grasping handles and pulling an object on a table – such that the experiments demonstrate that the joint relational-continuous model allows a robot to improve its execution based on experience, while reducing the severity of failures experienced during execution.

Export metadata

Additional Services


Show usage statistics
Document Type:Conference Object
Author:Alex MitrevskiORCiD, Paul G. Plöger, Gerhard Lakemeyer
Parent Title (English):2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 25-29, 2020, Las Vegas, NV, USA (Virtual)
First Page:5641
Last Page:5647
Date of first publication:2021/02/10
Award:IROS BEST PAPER on Cognitive Robotics
Abstract provided by the author.
Keyword:Cognitive robot control; Explainable robotics; Robot learning
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2020/07/01