Testing of an assistive robot system for haptic exploration of objects
Bibliographic record
Abstract
INTRODUCTION: When children with physical impairments cannot perform hand movements for haptic exploration, they may miss opportunities to learn the properties of objects. Assistive robots may enable them to make manipulation actions. OBJECTIVE: To examine the differences between using a robotic teleoperation system with haptic feedback and manual exploration when making perceptual comparisons about object properties. Accuracy and exploratory procedures (EP) using the system were compared to those in manual exploration. METHOD: Twenty adults without physical disabilities and ten typically developing children manipulated four pairs of objects and chose one based on size, roughness, hardness and shape. All participants completed the task with the robotic system (Tech) and manual exploration (No Tech), with the order counterbalanced. RESULTS AND CONCLUSION: Participants performed a previously unidentified EP, "tapping", in the Tech condition. Enclosure was not possible with the robot end effector, but tapping afforded the required perceptual information. Adults' perceptual comparisons were always accurate and they predominantly performed the optimum EP in both conditions. Even when children performed the optimum EP with the system, their answers were less accurate than with manual exploration. Most gave the correct answer, except for hardness, which was likely due to mechanical flexibility in the robotic system.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".