Ready—Aim—Fly! Hands-Free Face-Based HRI for 3D Trajectory Control of UAVs
Bibliographic record
Abstract
We present a novel user interface for aiming andlaunching flying robots on user-defined trajectories. The methodrequires no user instrumentation and is easy to learn by analogyto a slingshot. With a few minutes of practice users can sendrobots along a desired 3D trajectory and place them in 3D space, including at high altitude and beyond line-of-sight. With the robot hovering in front of the user, the robot tracksthe user's face to estimate its relative pose. The azimuth, elevationand distance of this pose control the parameters of the robot'ssubsequent trajectory. The user triggers the robot to fly thetrajectory by making a distinct pre-trained facial expression. Wepropose three different trajectory types for different applications:straight-line, parabola, and circling. We also describe a simple training/startup interaction to selecta trajectory type and train the aiming and triggering faces. Inreal-world experiments we demonstrate and evaluate the method. We also show that the face-recognition system is resistant to inputfrom unauthorized users.
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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) | 0.000 | 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".