Comparisons of data-driven models for detecting slip occurrence and direction based on simulations of tactile sensing
Bibliographic record
Abstract
Slip detection based on tactile sensing plays a crucial role for a robot to achieving stable grasps by promptly adjusting its grasping state. Detecting the occurrence of slippage has been a focus of previous research, but identifying the direction of slippage can provide richer reference information for correcting grasp issues. Due to the variations of experiment setup, the performances of these data-driven models have not been sufficiently compared. In this study, we compared the performance of three traditional machine learning algorithms—random forest, support vector machine, and k-nearest neighbors—with four deep learning models—gated recurrent unit, convolutional gated recurrent unit, convolutional long short-term memory, and 3D convolution—in detecting slippage states and directions. We conducted experiments using a simulated dataset collected in Gazebo. Additionally, we compared the noise resistance capability of each model and their generalization performance when facing new objects. The results show that when facing known grasped objects, all models can effectively detect slippage occurrences and their directions. However, as noise increases, Conv3D exhibits the strongest robustness. When generalized to unknown grasped objects, the deep learning models outperforms the three traditional learning algorithms, with CNN-GRU showing the best performance.
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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".