Stability Governor-guided RLMPC for Robot Manipulators
Bibliographic record
Abstract
Multi-joint manipulators hold significant potential across various applications; however, achieving optimized performance while ensuring constraint satisfaction remains challenging. To address this, a reinforcement learning-based model predictive control (RLMPC) framework is employed to optimize the manipulator’s motion while simultaneously tuning the terminal weighting. To reduce the computational burden and meet real-time requirements, the terminal constraint is removed from the optimization problem. However, the absence of a terminal constraint in conventional RLMPC frameworks necessitates a sufficiently large prediction horizon for convergence, since a longer horizon helps approximate the long-term cost and guides the system toward stability. Meanwhile, efficiently obtaining feasible samples in the state space remains challenging for manipulators. To overcome these limitations, a stability governor is introduced to generate a reference target at each time step, which enhances sampling efficiency and guides the RLMPC optimization toward a feasible solution that balances path efficiency and control performance. The proposed framework is validated through comparison simulations using a numerical model of the UR10e robot manipulator, demonstrating improved tracking performance, reduced computational complexity, and enhanced constraint satisfaction, showing its potential for real-world applications.
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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".