@inproceedings{27,
  author = {Melanie Jouaiti and Kerstin Dautenhahn},
  title = {What Kind of Player are You? Continuous Learning of a Player Profile for Adaptive Robot Teleoperation},
  abstract = {<p>Play is important for child development and robot-assisted play is very popular in Human-Robot Interaction as it creates more engaging and realistic setups for user studies. Adaptive game-play is also an emerging research field and a good way to provide a personalized experience while adapting to individual user’s needs. In this paper, we analyze joystick data and investigate player learning during a robot navigation game. We collected joystick data from healthy adult participants playing a game with our custom robot MyJay, while participants teleoperated the robot to perform goal-directed navigation. We evaluated the performance of both novice and proficient joystick users. Based on this analysis, we propose some robot learning mechanisms to provide a personalized game experience. Our findings can help improving human-robot interaction in the context of teleoperation in general, and could be particularly impactful for children with disabilities who have problems operating off-the-shelf joysticks.</p>
},
  year = {2022},
  journal = {IEEE International Conference on Development and Learning (ICDL)},
  pages = {68-74},
  month = {Sep.},
  doi = {10.1109/ICDL53763.2022.9962211},
}