Terrain Classification for a Quadrupedal Robot Using Proprioceptive Sensing
Bibliographic record
Abstract
Quadrupedal robots can traverse a wider range of terrain types than their wheeled counterparts, but these robots do not perform the same on all terrain types. These robots are prone to undesirable behaviours like sinking and slipping on challenging terrain. To combat this issue, we can implement a terrain classifier and use the output to compute the best path for the robot to navigate. The work presented here is a terrain classifier developed for a Boston Dynamics Spot robot. The quadruped provides us with over 104 measured signals describing the position and speed of the robot and each of its four legs. The developed terrain classifier combines dimensionality reduction techniques to extract the relevant information from the signals and then applies a classification technique to differentiate terrain based on traversability. The resulting terrain classifier can identify three different terrain types with an accuracy of 93%. Ongoing work aims to get the classifier running in real time and generate cost maps to inform future path planning.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".