Proof of Concept of an Assistive Robotic Arm Control Using Artificial Stereovision and Eye-Tracking
Bibliographic record
Abstract
Assistive robotic arms have become popular to help users with upper limb disabilities achieve autonomy in their daily tasks, such as drinking and grasping objects in general. Usually, these robotic arms are controlled with an adapted joystick. Joysticks are user-friendly when it comes to a general approach to an object. However, they are not as intuitive when having to accurately approach an object, especially when obstacles are present. Alternatively, the combined use of artificial stereovision and eye-tracking seems to be a promising solution, as the user's vision is usually dissociated from their upper limb disability. Hence, the objective of this study was to develop a proof of concept for the control of an assistive robotic arm using a low-cost combination of stereovision and eye-tracking. Using the developed control system, a typically developed person was able to control the robotic arm successfully reaching and grasping an object for 92% of the trials without obstacles with an average time of 13.8 seconds. Then, another set of trials with one obstacle had a success rate of 91% with an average time of 17.3 seconds. Finally, the last set of trials with two obstacles had a success rate of 98% with an average time of 18.4 seconds. Furthermore, the cost of an eye-tracker and stereovision remains below 400$.
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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 | 0.001 |
| 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 | 0.000 |
| 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".