Bibliographic record
Abstract
It has long been a challenge to control the end-effector trajectory of mechanical manipulators using only taskspace parameterization. While there exist some taskspace control strategies (such as the potential energy shaping) that guarantee the convergence of the end-effector to the target point without any inverse transformations, how to make the end-effector follow a desired path during the intermediate motion or to make it remain in certain subsets of taskspace, without resorting to inverse kinematics or inverse dynamics in one way or other, is still challenging. This paper studies the gyroscopic force as an auxiliary control action that can realize the desired motion profile of the end-effector without using any inverse transformations. Gyroscopic force is a specific type of force that does not affect the mechanical energy because its direction is always orthogonal to the velocity. This paper presents several ways to parameterize the artificial gyroscopic force with respect to the desired path profile of the end-effector as well as the desired velocity field that is often used as an indirect way to specify the target path. The formulated gyroscopic force is combined with the taskspace potential energy shaping control law to achieve the target tracking and the path following at the same time free from any inverse transformation. Simulation results are presented to validate the performance of proposed control strategies.
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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".