Pressing and Rubbing: Physics-Informed Features Facilitate Haptic Terrain Classification for Legged Robots
Bibliographic record
Abstract
Non-geometric hazards like sinkage and slipping, correlated to terrain categories, have an apparent effect on the locomotion of legged robots. Tactile-based terrain classification is a more accurate way to distinguish terrains in different properties than the vision, but selecting representative features instead of cumbersome ones in the complex foot-terrain interaction for efficient classification is still a challenge. In this letter, two specific leg motions are designed to inspect terrain bearing and friction properties, and manually designed features are extracted based on the foot-terrain interaction model for classification. These features are physics-informed, tidy and interpretable, and can be used with different classifiers under different foot configurations. Four classic classifiers with physics-informed features are trained for terrain classification and evaluated on our self-developed dataset. At the same time, the proposed method was compared with other two methods: an artificial feature extraction method and a CNN-based method. The results show that our proposed method reaches remarkable precision in terrain classification and can still guarantee a high accuracy under a small number of training samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".