Determining Shot Accuracy of a Robotic Pool System
Bibliographic record
Abstract
A process is described to determine the shot accuracy of an automatic robotic pool playing system. The system comprises a ceiling-mounted gantry robot, a special purpose cue end-effector, a ceiling-mounted camera, and a standard bar pool table. Two methods are compared for extracting the homography between the camera and the table plane. A challenge was the relatively large area of the table surface, which required a similarly large chessboard pattern to determine the point homography. In contrast, the Dual Conic method required only a set of orthogonal lines as a calibration target, which was more convenient to manipulate, and could be calculated from the integration of multiple images with multiple target locations. The Dual Conic method was shown experimentally to recover the homography with a similar, and sometimes greater accuracy than the Chessboard method. An experimental procedure was devised to measure the accuracy of an automatic shot using a sequence of images acquired from the overhead camera. For a set of 10 shots, the average absolute angular error in placing a shot was determined to be 0.74^0, with a standard deviation of 0.96^0.
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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".