Unsupervised learning for mobile robot terrain classification
Bibliographic record
Abstract
In this thesis, we consider the problem of having a mobile robot autonomously learn to perceive differences between terrains. The targeted application is for terrain identification. Robust terrain identification can be used to enhance the capabilities of mobile systems, both in terms of locomotion and navigation. For example, a legged amphibious robot that has learned to differentiate sand from water can automatically select its gait on a beach: walking for sand, and swimming for water. The same terrain information can also be used to guide a robot in order to avoid specific terrain types. The problem of autonomous terrain identification is decomposed into two sub-problems: a sensing sub-problem, and a learning sub-problem. In the sensing sub-problem, we look at extracting terrain information from existing sensors, and at the design of a new tactile probe. In particular, we show that inertial sensor measurements and actuator feedback information can be combined to enable terrain identification for a legged robot. In addition, we describe a novel tactile probe designed for improved terrain sensing. In the learning sub-problem, we discuss how temporal or spatial continuities can be exploited to perform the clustering of both time-series and images. Specifically, we present a new algorithm that can be used to train a number of classifiers in order to perform clustering when temporal or spatial dependencies between samples are present. We combine our sensing approach with this clustering technique, to obtain a computational architecture that can learn autonomously to differentiate terrains. This approach is validated experimentally using several different sensing modalities (proprioceptive and tactile) and with two different robotic platforms (on a legged robot named AQUA and a wheeled robot iRobot Create). Finally, we show that the same clustering technique, when combined with image information, can be used to define a new image segmentation algorithm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".