Commodifying Pointing in HRI: Simple and Fast Pointing Gesture Detection from RGB-D Images
Bibliographic record
Abstract
We present and characterize a simple and reliable method for detecting pointing gestures suitable for human-robot interaction applications using a commodity RGB-D camera. We exploit an existing Deep CNN model to robustly find hands and faces in RGB images, then examine the corresponding depth channel pixels to obtain full 3D pointing vectors. We test several methods of estimating the hand end-point of the pointing vector. The system runs at better than 30Hz on commodity hardware: exceeding the frame rate of typical RGB-D sensors. An estimate of the absolute pointing accuracy is found empirically by comparison with ground-truth data from a VICON motion-capture system, and the useful interaction volume established. Finally, we show an end-to-end test where a robot estimates where the pointing vector intersects the ground plane, and report the accuracy obtained. We provide source code as a ROS node, with the intention of contributing a commodity implementation of this common component in HRI systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".