Coordination of hydraulic manipulators by reinforcement learning
Bibliographic record
Abstract
In this paper, a reinforcement learning method is applied to coordinate a pair of horizontal hydraulic actuators engaged in the cooperative positioning of an object. The goal is to enable the actuators to discover how to intelligently select control actions that tend to reduce the interaction forces directed along the axis of motion, while maintaining the desired trajectory. First, a detailed and realistic dynamic model of the entire system is derived. A multi-layer reinforcement learning neural network control architecture is designed next to regulate the interaction force during positioning. To regulate the interaction force, the neural network measures the interaction force and proposes a modification to the a priori prescribed formation constrained position trajectory. Each actuator system is outfitted with such a neural controller so that a decentralized reinforcement learning control system results. Simulations demonstrate the efficacy of the approach towards reducing the interaction forces and minimizing the associated object internal force in a single degree of freedom
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".