Texture Recognition on Uneven Surfaces Using Deep Learning and Tactile Sensing Techniques
Bibliographic record
Abstract
Tactile texture recognition plays a crucial role in robotic systems, enhancing their ability to interpret and interact with diverse environments. This research aims to address the challenge of tactile texture classification on both even and uneven surfaces by utilizing advanced deep learning models. By integrating 1D convolutional neural networks (CNN), bidirectional long short-term memory (LSTM), and hybrid architectures, we focus on improving classification accuracy and processing efficiency. The methodology involves collecting tactile data from the Open Manipulator X equipped with MARG and barometer sensors, applying a sliding window approach for multi-scale analysis. Our best model, trained on time-series data, achieved the accuracy, precision, and recall rate of $97.19 \%, 92.28 \%$, and $97.19 \%$ respectively for even surfaces, and $92.11 \%, 92.99 \%$, and $92.11 \%$ respectively for uneven surfaces. These results highlight the effectiveness of deep learning techniques in tactile texture recognition, particularly on complex, uneven geometries. This study advances tactile perception in robotics, enabling systems to navigate and interact more effectively with diverse, real-world environments, making precise tactile recognition critical.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".