Nonlinear Contact Parameter Estimation for Robotic Systems With Payload
Bibliographic record
Abstract
Simulating robotic operations where the robot interacts with its environment remains a challenging task because of the difficulties involved in contact modeling. At present, the basic methodologies for modeling contact are well established and have been integrated into many existing multibody dynamics formulations and software. One popular approach involves modeling the normal contact force as a continuous function of deformation, according to a particular constitutive relation. These models are usually simple in form and are easily integrated into any multibody framework because they provide an explicit relationship between the normal contact force and a geometric penetration variable, with the aid of appropriate contact parameters. One issue that remains ambiguous, however, is the choice of the contact parameters to be ‘fed’ into the force-deformation law. In this paper, the problem of contact parameter estimation is addressed in the context of a nonlinear contact force model proposed by Hunt and Crossley. An offline parameter estimation algorithm is developed which effectively transforms the nonlinear estimation problem into a linear one, it in turn solved using a multi-pass recursive technique. Results of application of the algorithm to simulated and experimental data are presented, the latter obtained with a six-DOF robotic manipulator and a variety of payload materials and geometries. Comparison of the proposed method to the nonlinear curve fitting algorithm from MATLAB demonstrates some advantages and limitations.
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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".