@inproceedings{RomeroKramerOrtegaSainzMitrevskietal.2019,
  author    = {Erick Romero Kramer and Argentina Ortega Sainz and Alex Mitrevski and Paul G. Pl{\"o}ger},
  title     = {Tell Your Robot What To Do: Evaluation of Natural Language Models for Robot Command Processing},
  series = {Chalup, Niemueller et al. (Eds.): RoboCup 2019: Robot World Cup XXIII. Proceedings of the 23rd RoboCup International Symposium, 8 July 2019, Sydney, Australia. Lecture Notes in Computer Science (LNCS), Vol 11531},
  publisher = {Springer International Publishing},
  address   = {Cham},
  isbn      = {978-3-030-35698-9},
  doi       = {10.1007/978-3-030-35699-6\_20},
  pages     = {255 -- 267},
  year      = {2019},
  abstract  = {The use of natural language to indicate robot tasks is a convenient way to command robots. As a result, several models and approaches capable of understanding robot commands have been developed, which however complicates the choice of a suitable model for a given scenario. In this work, we present a comparative analysis and benchmarking of four natural language understanding models - Mbot, Rasa, LU4R, and ECG. We particularly evaluate the performance of the models to understand domestic service robot commands by recognizing the actions and any complementary information in them in three use cases: the RoboCup@Home General Purpose Service Robot (GPSR) category 1 contest, GPSR category 2, and hospital logistics in the context of the ROPOD project.},
  language  = {en}
}