Online Identification of Environment Hunt–Crossley Models Using Polynomial Linearization
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Bibliographic record
Abstract
Online environment dynamic estimates are often used for the control of robots, telerobots, and haptic systems. The nonlinear Hunt-Crossley (HC) model, which is physically consistent with the behavior of soft objects with limited deformation at a single point of contact, is being increasingly used in robotic control systems. The HC model can be identified online using a single-stage log linearization technique; however, the accuracy and applicability of the existing method is limited. We propose a two-stage polynomial identification method, which uses a quadratic approximation in the first stage to generate a linearly parameterized model of the HC dynamics (Quad-Poly). The coefficients of the Quad-Poly model are then used in the second stage to extract the HC parameters using a lookup table and recursive least squares parameter estimation. The proposed method is experimentally assessed against a previous natural logarithm linearization method, and further tested for time-varying environment dynamics and human-generated trajectories and for robustness against uncertainties in the measured data and system parameters.
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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 it