Gaze2Instruct (G2I): Towards a More Inclusive Language-Conditioned Robotic Assistance for Severe Speech and Motor Impairments
Bibliographic record
Abstract
People with severe speech and motor impairment (SSMI) often require assistive technologies to control their environment, including robots. Current eye-gaze-controlled robotic systems, however, are limited in scope, focusing on specific tasks or requiring structured command sequences. In this work, we introduce Gaze2Instruct (G2I), a novel approach for predicting the intentions of people with SSMI. G2I leverages eye-gaze data and visual input to automatically generate natural language instructions, which can then be interpreted by existing language-conditioned robotics. By translating eye-gaze into versatile language commands, we enable intuitive interaction with assistive robots for individuals with SSMI, allowing for unstructured, real-time task execution without predefined grammar or task-specific solutions, leveraging the power of segmentation models and Multimodal Large Language Models (MLLM). Through a series of experiments, we demonstrate the effectiveness of our system in generating accurate and meaningful instructions, reducing cognitive load, and improving the ease of interaction for users with SSMI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".