Improving Robotic Force Control Performance in Devices With Force Measurement and Modeling Error
Bibliographic record
Abstract
This paper investigates methods for improving robot force control performance in situations where force measurement error is unavoidable, such as exoskeletons, low-cost sensors, and flexible robotics. When using force control strategies like Impedance control (IC) and Admittance control (AC) in these situations, we propose that it is important to consider their inherent robustness to either force measurement error or modelling error. Improper choice of controller can lead to increased error, undesired interaction behaviour, and even instability. Furthermore, we propose that interpolation with Unified Interaction control (UIC) can trade-off these two robustness properties and, in some cases, can improve performance. A case study using a low-cost force sensitive resistor with less accuracy than traditional force/torque sensors is conducted. The results illustrate that, even when force measurements do not reach the accuracy of traditional sensors, an accurate rendering of the desired impedance can be achieved by careful selection of the controller.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".