@inproceedings{AbdelrahmanMitrevskiPloeger2020,
  author    = {Ahmed Faisal Abdelrahman and Alex Mitrevski and Paul G. Pl{\"o}ger},
  title     = {Context-Aware Task Execution Using Apprenticeship Learning},
  series = {Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), 31 May - 31 August, 2020, Paris, France},
  publisher = {IEEE},
  isbn      = {978-1-7281-7395-5},
  doi       = {10.1109/ICRA40945.2020.9197476},
  pages     = {1329 -- 1335},
  year      = {2020},
  abstract  = {An essential measure of autonomy in assistive service robots is adaptivity to the various contexts of human-oriented tasks, which are subject to subtle variations in task parameters that determine optimal behaviour. In this work, we propose an apprenticeship learning approach to achieving context-aware action generalization on the task of robot-to-human object hand-over. The procedure combines learning from demonstration and reinforcement learning: a robot first imitates a demonstrator’s execution of the task and then learns contextualized variants of the demonstrated action through experience. We use dynamic movement primitives as compact motion representations, and a model-based C-REPS algorithm for learning policies that can specify hand-over position, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We additionally conduct a user study involving participants assuming different postures and receiving an object from a robot, which executes hand-overs by either imitating a demonstrated motion, or adapting its motion to hand-over positions suggested by the learned policy. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.},
  language  = {en}
}