The role of haptic feedback when manipulating nonrigid objects
Bibliographic record
Abstract
Humans can learn to manipulate objects with complex dynamics, including nonrigid objects with internal degrees of freedom. The first aim of this study was to assess the contribution of haptic feedback when learning to manipulate a nonrigid object. The second aim was to evaluate how learning without haptic feedback influences subsequent learning with haptic feedback and vice versa. The task involved moving a simulated mass-attached to a grasped handle via a simulated, damped spring-to a target as quickly as possible. In the haptic plus vision (HV) condition, appropriate forces were applied to the handle, which was attached to a robot. In the vision only (V) condition, these forces were turned off. Participants completed 80 trials in each condition, with one-half starting with the HV condition. Both groups exhibited significant learning, as measured by movement time, in both conditions. For the condition performed first, initial performance, learning rate, and final performance were better with haptic feedback. Prior experience in the HV condition led to faster learning and better final performance in the V condition. However, prior experience in the V condition led to slower learning and worse final performance in the HV condition. In the V condition, all participants tended to keep the mass close to the hand. In the HV condition, participants who started with the HV condition allowed the mass to move away from the hand, whereas participants who started with the V condition continued to keep the mass close to the hand. We conclude that haptic feedback as well as prior experience with haptic feedback enhance the ability to control nonrigid objects and that training without haptic feedback can lead to persisting detrimental effects when subsequently dealing with haptic feedback.
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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".