Enhancing tactile texture recognition from haptic surface reconstruction using reinforcement learning
Bibliographic record
Abstract
Tactile texture recognition is a crucial skill for humans, but it is challenging to emulate in robots. This is mainly due to the complexities of detecting and analyzing textures on uneven or irregular surfaces. This paper introduces a novel approach that leverages haptic surface reconstruction combined with reinforcement learning (RL) to enhance robotic tactile texture recognition. Our method involves an initial haptic surface reconstruction refined through RL to estimate contact points and surface normals accurately. A robot equipped with a multimodal tactile sensing module then uses this information to explore surfaces, collecting data instrumental for texture identification. For the texture recognition phase, we employ two types of recurrent neural networks (RNNs): one with a feature encoder and one without. Our findings demonstrate that the RL-refined trajectories significantly improve classification accuracy from 63.46% to 89.58%. Combining haptic feedback and reinforcement learning significantly improves robotic texture classification on uneven surfaces, irrespective of the classifiers employed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".