Hardware Agnostic Robotic Hand Algorithms with Recreational, Educational and Healthcare Applications
Bibliographic record
Abstract
With a mockup prototype of a robotic hand, various algorithms were implemented. This exposed a broad range of applications of the prototype. In this body of work, algorithms of a dexterous mechanical robotic hand will be presented which span areas including recreation, healthcare and education. Imagine a child learning to count. If this hypothetical child had a robotic assistant who could infinitely demonstrate the way to count without getting tired, consequently the child would learn how to count at a much-accelerated rate. Perhaps in an arcade there is a robotic hand that plays games like rock paper scissors or thumb wars, subsequently the youth will be further inspired to pursue an understanding of robotic systems. At the Tech Crunch Robotic conference on July 21st, Dean Kamen the inventor of the segway said something very profound, it was along the lines of how when it comes to interest in Robotics, we are competing with activities like after school sports or video games because these are the things that consume the time of children. However, robotics is mostly seen as an academic activity when it must not be so. With the changing job market, equipping arcade games with intelligent robots is not a farfetched idea. Another algorithm implemented was a sign language algorithm where the robotic hand executes a sequence of hand-signs in a desired order. The programming was done with Adafruit's PCA9685 controller which can control 16 servo motors through an I2C interface. When all I2C buses are used then the controller can control up to 992 servo motors.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".