Adaptive Control of Haptic Interaction with Impedance and Admittance Type Virtual Environments
Bibliographic record
Abstract
Adaptive nonlinear controllers have been proposed to improve the stability and transparency in haptic rendering. Through a separation of control and dynamic simulation, the proposed controllers can couple impedance-type haptic devices with impedance and admittance-type virtual environment simulators. The intervening dynamics of the interface, subject to stability constraints, can be replaced with an adjustable mass-damper tool within the proposed framework. Nonlinear dynamics for haptic device and parametric uncertainty in user's arm dynamics are considered in the design of controllers which require position, velocity and force measurements. The transparency and stability of the proposed haptic control systems are investigated using a Lyapunov analysis. The controllers are implemented on a two-axis impedance-type haptic device for interacting with impedance and admittance-type virtual environments. In the impedance-type environment, interaction with a virtual wall is modeled by a spring-damper coupler. This model along with an alternative constraint-based rigid wall model are employed in the admittance-type simulations. Although the two controllers behave similarly in free motion, the controller for admittance-type environments is capable of rendering markedly stiffer rigid contacts.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 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".