Real-time control experiments using an industrial robot retrofitted with an open-structure controller
Bibliographic record
Abstract
A robotic testbed has been developed in our laboratory by retrofitting a PUMA 560 industrial robot with a custom-built controller. The main objective of the testbed is to provide researchers with a robot control system possessing an open hardware structure, high computational capacity, and good software programmability, such that various advanced control schemes intended for robots can be implemented in real-time and evaluated through physical experiments rather than simulation. On this testbed, different control schemes including regressor based adaptive control of the robot performing tracking tasks are implemented. Dynamics of robot actuators are incorporated in the experiments. The performance of these control schemes is quantitatively evaluated. For comparison purposes, a conventional PD controller is also implemented independently in each joint of the robot. This paper outlines the hardware configuration and software support of the testbed. Adaptive control scheme incorporating actuator dynamics is described and experimental results are presented. These experiments show that advanced control schemes can be effectively used to improve the performance of an industrial robot in a significant manner.>
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".