Reinforcement learning based dynamic path following of an industrialrobot
Why this work is in the frame
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Bibliographic record
Abstract
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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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 | 0.000 |
| 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 it