A Hybrid Approach to Force Balancing of Robotic Mechanisms
Bibliographic record
Abstract
A hybrid approach to force balancing of robotic mechanisms which have at least two degrees of freedom is proposed. This hybrid approach is to combine adjusting kinematic parameters (AKP) and counterweights (CW) approaches, and it is called AKP+CW in short. The main motivation of the AKP+CW approach is that CW and AKP each has its own advantage and disadvantage, and thus a combined one may strengthen the both. This paper presents the force balancing principles and equations for the AKP+CW approach. Software called ADAMS is employed as a tool for the simulated experiment to verify the effectiveness of the proposed approach. The implication of the work described in this paper to the balancing of mechanisms in general is that many different force balancing methods may be combined based on the hybridization principle proposed in this paper to become a novel one.
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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".