Eye-In-Hand Visual Servoing for Accurate Shooting in Pool Robotics
Bibliographic record
Abstract
Deep Green is a robotic pool system whose objective is to play the game of pool competitively against skillful human opponents. To enhance the playing accuracy of the system, a visual servoing algorithm using a wrist-mounted camera was developed to correct the absolute positioning error of the robot. The novel technique considers an ideal line defined by the intersection of the object and cue ball centers at their ideal locations from the vantage of this camera. The ideal line represents an imaginary straight shot trajectory projected onto the image plane. The transformation of 2-D image points into 3-D robotic motions is done through estimation (in the image-based approach) or by a 2-D to 3-D spatial mapping (in the position-based approach). While the first option is simpler, the later is more effective, requiring fewer iterations and therefore less time to converge. Experiments were designed to measure the accuracy of the system. Using the wrist-mounted camera, the system increased its shooting accuracy by a factor of three, with high consistency.
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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 | 0.001 |
| 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".