Lagrange Dynamic Modeling of a Multi-Fingered Robot Hand in Free Motion Considering the Coupling Dynamics
Bibliographic record
Abstract
Multi-fingered robot hands have been one of the major research topics because several robotic systems, including service robots, industrial robots and wheel-type mobile robots require grasping and manipulation of a variety of objects as crucial functionalities. Roughly speaking, there are two different types of robotic behavior: free motion, purpose of this paper and constrained motion that would be published in the near future. In this paper, we address the problem of multi-fingered robot hand’s dynamic modeling which is fundamental in design of model-based controllers for grasping and manipulation tasks. Based on the specified multi-fingered robot hand, a new methodology for deriving an efficient dynamic equation by the Lagrange formulation is presented. This methodology is new in the sense that it considers the coupling dynamics of the system in the identification of the parameters of the dynamic equation. Furthermore the developed dynamic model leads to decoupling dynamic characteristics, by which the control of different parts of the system can be separately simulated. So the new structure of the dynamic model was very useful and effective for the simulation and the diagnostic. Several simulation results proved that the derived dynamic model can predict the motion of the multi-fingered hand in free motion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".