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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".