Force distribution of power grasps based on the controllability of contact forces
Bibliographic record
Abstract
An analysis of force distributions for grasping by robot hands requires determination of contact forces between the fingers and the grasped object. This topic has been extensively investigated for fingertip grasps under the assumption that the fingers have enough joints so that contact forces can be actively controlled. This assumption is, however, not valid in power grasps for which the internal forces may have uncontrollable components. To ensure that the contact forces can be obtained by controlling the joint torques, the uncontrollable components must be separated from the internal forces. In this paper, a method is obtained to identify those power grasps for which the internal forces have uncontrollable components. A new formulation of contact forces is developed for the general solution to the force distribution of power grasps and provides a framework for optimization. The advantage of the approach is that the joint torques are explicitly considered, and, therefore, optimal contact forces can be achieved by modifying the joint torques.
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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) | 0.001 | 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".