Combination of eyetracking and computer vision for robotics control
Bibliographic record
Abstract
The manual control of manipulator robots can be complex and time consuming even for simple tasks, due to a number of degrees of freedom (DoF) of the robot that is higher than the number of simultaneous commands of the joystick. Among the emerging solutions, the eyetracking, which identifies the user gaze direction, is expected to automatically command some of the robot DoFs. However, the use of eyetracking in three dimensions (3D) still gives large and variable errors from several centimeters to several meters. The objective of this paper, is to combine eyetracking and computer vision to automate the approach of a robot to its targeted point by acquiring its 3D location. The methodology combines three steps : - A regular eyetracking device measures the user mean gaze direction. - The field of view of the user is recorded using a webcam, and the targeted point identified by image analysis. - The distance between the target and the user is computed using geometrical reconstruction, providing a 3D location point for the target. On 3 trials, the error analysis reveals that the computed coordinates of the target 3D localization has an average error of 5.5cm, which is 92% more accurate than using the eyetracking only for point of gaze calculation, with an estimated error of 72cm. Finally, we discuss an innovative way to complete the system with smart targets to overcome some of the current limitations of the proposed method.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".