@inproceedings{MitrevskiPadalkarNguyenetal.2019,
  author    = {Alex Mitrevski and Abhishek Padalkar and Minh Nguyen and Paul G. Pl{\"o}ger},
  title     = {\"Lucy, Take the Noodle Box!\": Domestic Object Manipulation Using Movement Primitives and Whole Body Motion},
  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\_15},
  pages     = {189 -- 200},
  year      = {2019},
  abstract  = {For robots acting - and failing - in everyday environments, a predictable behaviour representation is important so that it can be utilised for failure analysis, recovery, and subsequent improvement. Learning from demonstration combined with dynamic motion primitives is one commonly used technique for creating models that are easy to analyse and interpret; however, mobile manipulators complicate such models since they need the ability to synchronise arm and base motions for performing purposeful tasks. In this paper, we analyse dynamic motion primitives in the context of a mobile manipulator - a Toyota Human Support Robot (HSR)- and introduce a small extension of dynamic motion primitives that makes it possible to perform whole body motion with a mobile manipulator. We then present an extensive set of experiments in which our robot was grasping various everyday objects in a domestic environment, where a sequence of object detection, pose estimation, and manipulation was required for successfully completing the task. Our experiments demonstrate the feasibility of the proposed whole body motion framework for everyday object manipulation, but also illustrate the necessity for highly adaptive manipulation strategies that make better use of a robot's perceptual capabilities.},
  language  = {en}
}