Artificial tactile perception for surface identification using a triple axis accelerometer probe
Bibliographic record
Abstract
In recent years, autonomous robots have been increasingly deployed in unknown environments. In order to cope with the unknown, the capability to train autonomously the perception model of an environment is highly desirable. By developing proper sensing technology, this task can be significantly facilitated. In this paper, we explore the problem of artificial tactile perception, aimed at surface identification. To this end, we introduce a simple tactile probe based upon triple axis accelerometers. This tactile probe was tested on a large collection (28) of flat surfaces, using a controlled test bed. In a first set of experiments, we demonstrated the discrimination capabilities of the probe, by achieving a surface recognition rate of 96.7% with 1 second of data, using a Support Vector Machine classifier. We also demonstrate that similar results can be achieved without the need for ground truth or the actual number of surfaces using Dirichlet process mixture models, a Bayesian nonparametric approach. These two experiments indicate that tactile sensing is, thus, a potentially viable solution for autonomous surface identification.
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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) | 0.000 | 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 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".