@inproceedings{MitrevskiPloegerLakemeyer2021,
  author    = {Alex Mitrevski and Paul G. Pl{\"o}ger and Gerhard Lakemeyer},
  title     = {Representation and Experience-Based Learning of Explainable Models for Robot Action Execution},
  series = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 25-29, 2020, Las Vegas, NV, USA (Virtual)},
  publisher = {IEEE},
  isbn      = {978-1-7281-6212-6},
  doi       = {10.1109/IROS45743.2020.9341470},
  pages     = {5641 -- 5647},
  year      = {2021},
  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 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.},
  language  = {en}
}