Predictive haptic guidance: intelligent user assistance for the control of dynamic tasks
Bibliographic record
Abstract
Intelligent systems are increasingly able to offer real-time information relevant to a user's manual control of an interactive system, such as dynamic system control space constraints for animation control and driving. However, it is difficult to present this information in a usable manner and other approaches which have employed haptic cues for manual control in "slow" systems often lead to instabilities in highly dynamic tasks. We present a predictive haptic guidance method based on a look-ahead algorithm, along with a user evaluation which compares it with other approaches (no guidance and a standard potential-field method) in a 1-DoF steered path-following scenario. Look-ahead guidance outperformed the other methods in both quantitative performance and subjective preference across a range of path complexity and visibility and a force analysis demonstrated that it applied smaller and fewer forces to users. These results (which appear to derive from the predictive guidance's supporting users in taking earlier and more subtle corrective action) suggest the potential of predictive methods in aiding manual control of dynamic interactive tasks where intelligent support is available.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".