Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE International Conference on Development and Learning (ICDL) |
| Publisher | IEEE |
| Pages | 68-74 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781665413114, 9781665413107 |
| ISBN (Print) | 9781665413121 |
| DOIs | |
| Publication status | Published - 30 Nov 2022 |
| Event | 2022 IEEE International Conference on Development and Learning (ICDL) - London, United Kingdom Duration: 12 Sept 2022 → 15 Sept 2022 |
Conference
| Conference | 2022 IEEE International Conference on Development and Learning (ICDL) |
|---|---|
| Period | 12/09/22 → 15/09/22 |
Bibliographical note
Presented 15 Sept 2022 at IEEE International Conference on Development and Learning (ICDL)Keywords
- Measurement
- Navigation
- Human-robot interaction
- Games
- User experience
- Robot learning
- Robots
