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

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5641–5647

Abstract

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 practically useful only 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.

Authors 3

1. Alex Mitrevski

   Hochschule Bonn-Rhein-Sieg

   Affiliation as printed

   Department of Computer Science, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, Germany

2. Paul G. Plöger

   Hochschule Bonn-Rhein-Sieg

   Affiliation as printed

   Department of Computer Science, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, Germany

3. Gerhard Lakemeyer Aachen Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

   Department of Computer Science, RWTH Aachen, Aachen, Germany