BioIn-Tacto: A compliant multi-modal tactile sensing module for robotic tasks
Bibliographic record
Abstract
We present the open-source design and fabrication of a compliant multimodal tactile sensing module. The sensing module design presented here enables robotic end-effectors to sense contact properties like pressure and vibration and estimate a quaternion that represents the deformation due to contact. We elaborated the module's compliant structure fabrication process to use only 3D printed molds and a vacuum chamber, making it accessible to a broad range of roboticists. The process also allows production batches of up to five modules and easy deployment to robotic hands. The repository associated contains packages that support the Robot Operating System (ROS) to perform data collection and visualization. All hardware Computer-Aided Design (CAD) files and software source codes have been released and can be easily assembled and modified. The sensing module proposed in this paper uses off-the-shelf Microelectromechanical (MEMs) barometers and Magnetic, Angular Rate and Gravity (MARG) systems. Those components may be replaced by alternatives depending on the sensor's availability, constraints related to the module's size, or robotic application without prejudice to sensor functionality.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".