Application of neural networks in inverse dynamics based contact force estimation
Why this work is in the frame
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Bibliographic record
Abstract
In the majority of robotic applications, including manipulation and human-robot interaction, contact force needs to be monitored and controlled. Compliance controllers demand high precision force measurement that can be delivered by commercial force/torque sensors. However, these sensors are expensive, rather bulky and vulnerable to impact forces. A common solution to this dilemma is the use of force observers, which estimate external forces using full knowledge about system dynamics. However, some robotic systems have complicated dynamics that may or may not be known entirely and precisely. In these situations the implementation of dynamic observers would not result in accurate force estimation. This paper proposes the use of neural networks in an inverse dynamics based force observation without the need for complete determination of system dynamics. We also show that for slow operations on soft environments, the observer estimates external forces without acceleration input
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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