A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modules
Bibliographic record
Abstract
Robots need to adapt to the complexities of acting in unstructured environments. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, visibility, and lack of information, have diverted some focus to tactile sensing. Extensive datasets of the physical interactions between tactile-enabled robots are required to investigate and develop methods for performing manipulation and object exploration tasks. Therefore, this motivates us to compose a dataset of signals from Bioin-Tacto modules mounted on a robotic gripper performing extraction tasks. An operator controls a robotic gripper to extract three pegs of various complexities from their corresponding holes. This dataset contains angular velocity, linear acceleration, magnetic field intensity and direction, and pressure exerted on two tactile modules embedded in the compliant structure of the sensing module. The dataset comprises 96 extraction episodes, including data collected from a reinforcement learning agent. The dataset can be used to pre-train a reinforcement machine learning model to perform peg-in-hole tasks and to study how pretraining affects a manipulator's ability to infer tactile signals and improve the success rates of the manipulator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".