Humans Versus Robots: Converting Golf Putter Trajectories for Robotic Guidance
Bibliographic record
Abstract
Robotic devices are used to provide physical guidance when teaching different movements. To advance our knowledge of robotic guidance in training complex movements, this investigation tested different kinematic data filtering methods of individual’s golf putts to convert them into trajectories to be employed by a robot arm. The purpose of the current study was to identify a simple filtering method to aptly replicate participants’ individual golf putter trajectories which could be used by the robot to execute them with greater consistency and accuracy than their human counterpart. Participants putted toward three targets where three-dimensional data of the putter’s head were filtered and then fitted by using one or two dimensions of the participant’s putter head trajectories. As expected, both filtering methods employed with the robot outperformed the human participants in ball endpoint accuracy and consistency. Further, after comparing the filtered to the human participants’ trajectories, the two-dimensional method best replicated the kinematic features of human participants’ natural putter trajectory, while the one-dimensional method failed to replicate participant’s backstroke position. This investigation indicates that a two-dimensional filtering method, using Y -forward and Z -vertical position data, can be used to create accurate, consistent, and smooth trajectories delivered by a robot arm.
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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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".