Real-Time Identification of Hunt–Crossley Dynamic Models of Contact Environments
Bibliographic record
Abstract
Real-time estimates of environment dynamics play an important role in the design of controllers for stable interaction between robotic manipulators and unknown environments. The Hunt-Crossley (HC) dynamic contact model has been shown to be more consistent with the physics of contact, compared with the classical linear models, such as Kelvin-Voigt (KV). This paper experimentally evaluates the author's previously proposed single-stage identification method for real-time parameter estimation of HC nonlinear dynamic models. Experiments are performed on various dynamically distinct objects, including an elastic rubber ball, a piece of sponge, a polyvinyl chloride (PVC) phantom, and a PVC phantom with a hard inclusion. A set of mild conditions for guaranteed unbiased estimation of the proposed method is discussed and experimentally evaluated. Furthermore, this paper rigorously evaluates the performance of the proposed single-stage method and compares it with those of a double-stage method for the HC model and a recursive least squares method for the KV model and its variations in terms of convergence rate, the sensitivity to parameter initialization, and the sensitivity to the changes in environment dynamic properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".