What Kind of Player are You? Continuous Learning of a Player Profile for Adaptive Robot Teleoperation
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".