Personalized Adaptive Virtual Object Placement in AR for Nonspeaking Autistic Users Using Behavioural Cloning
Bibliographic record
Abstract
Nonspeaking autistic individuals (“nonspeakers”) represent about one-third of the autistic population, yet most lack access to an effective alternative to speech. This lack of effective communication significantly limits their access to educational, social, and employment opportunities. Some nonspeakers have learned to spell words and sentences by pointing to letters on a physical letterboard held in their field of view by a trained human assistant. While effective, this method relies on the assistant for positioning the letterboard, limiting user autonomy and privacy. We report here a system we developed that uses Behavioral Cloning (BC) to automatically and adaptively position a virtual letterboard in Augmented Reality (AR). By observing finger, palm, head, and physical letterboard poses during real-life interactions between a nonspeaker and their assistant, we train a BC Machine Learning (ML) model that can adapt the placement of a virtual letterboard for that user. Results from 11 experiments (3 emulated scenarios and 8 nonspeaking autistic participants) show that our approach can accurately replicate the actions of the human assistant of any given user, outperforming a non-ML baseline personalized placement policy in both positional and rotational accuracies. Further, our novel BC formulation overcomes traditional data-efficiency limitations, allowing us to achieve high accuracy with a modest training effort. This work represents a foundational step toward enabling more autonomous and private communication for nonspeakers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".