Reinforcement learning based dynamic path following of an industrialrobot
Bibliographic record
Abstract
Enhancing industrial robot path following accuracy requires the real-time feedback of an external sensor.This study introduces a position-based visual servoing (PBVS) scheme to decrease path error by correcting the Cartesian pose in real-time.The vision system estimates the end effector pose from which the Cartesian pose offset is calculated.The robot's internal control system treats the pose offset as a high-level control input and induces real-time modification of the robot's intrinsic motion.A proportional-integral-derivative (PID) controller is utilized as the baseline control method.Due to the repetitiveness of robot tasks, the control performance undergoes iterative improvement via the supplementation of a reinforcement learning(RL)-based controller trained via a state-of-the-art actor-critic algorithm.The experimental platform comprises two commercial systems: the C-Track 780 dual camera sensor from Creaform and the M-20iA robot from FANUC.In a position-only line following experiment, the effect of RL-based controller supplementation significantly enhances path accuracy by attenuating overshoot.The mean absolute error (MAE) and the maximum error are reduced by 10% and 20%, respectively.In terms of the Euclidean norm, the maximum path error is 0.09 mm.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".