Supporting Play by Applying Haptic Guidance Along a Surface Learnt from Single Motion Trajectories
Bibliographic record
Abstract
Haptic-enabled teleoperated robots can help children with physical disabilities to reach toys by applying haptic guidance towards their toys, thus compensating for their limitations in reaching and manipulating objects. In this article we preliminarily tested a learning from demonstration (LfD) approach, where a robotic system learnt the surface that best approximated to all motion trajectories demonstrated by the participants while playing a whack-a-mole game. The end-goal of the system is for therapists or parents to demonstrate to it how to play a game, and then be used by children with physical disabilities. In this study, four adults without disabilities participated, to identify aspects that will be necessary to improve before conducting trials with children. During the demonstration phase, participants played the game in normal teleoperation, assuming the role of the therapist/parent. Then, the surface was modeled using a neural network. Participants played the game without and with the haptic guidance. The movements of the robotic system were mirrored to induce errors in movements, and thus require the guidance. Participants spent more time, moved the robot longer distances, and had jerkier movements when they played the game with the guidance than without it. Possible reasons were discussed, and several solutions were proposed to improve the system. The main contribution of this paper was the learning of a surface instead of learning a single motion trajectory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".