Building a Classifier Model for Failure Modes from Robot Sensor Readings through a Modified Forward Stepwise Algorithm
Bibliographic record
Abstract
One of the many challenges in autonomous robots is that they can enter an error state and are unable to continue operation without human intervention. Sensors in-stalled on the robot enable proprioception and could help the robot understand its error configuration. This paper proposes a method to determine from these sensor measurements, which are most critical in differentiating the error states such that the robot could understand its predicament, and could attempt at recovering without human aid. A classification model is built using the forward stepwise method and a scoring metric to overcome indecision in choosing between different features. This modified method is applied to three robot operating mode data sets. The experiments indicate an improvement to the classifier performance when using this the model built by the method compared to using all available predictor variables (features). With further refinement, this scoring metric could be a simple yet effective way to build classification models for increasing robot autonomy.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".