Using Potential Field Function With a Velocity Field Controller to Learn and Reproduce the Therapist's Assistance in Robot-Assisted Rehabilitation
Bibliographic record
Abstract
Rehabilitative and assistive practices usually elicit intense and repetitive exercises. Thus, there has been an increasing interest in robotic systems as they are robust and cost-effective in comparison to conventional physical motor-therapy with a therapist. These robots have applications in therapeutic and in-home environments, where there is a necessity for a user-friendly procedure to program the robots for a specific task easily. Our group has suggested robot learning from demonstration (LfD) as an intuitive procedure to program robots via short-term physical interaction in rehabilitation and assistive applications. In this article, a therapist assists a patient, and cooperatively performs a task on a robotic manipulator. Then, using a nonparametric potential field function, the therapist's motion, and interaction force (assistance/resistance) is modeled time-independently via a convex optimization algorithm. Next, in the therapist's absence, the robot provides the patient with the same level of interaction force provided by the therapist along the trajectory. A velocity field controller is also designed to compensate and regulate the patient's deviation from the velocity observed in the demonstration phase. Finally, the efficacy, advantages, and stability of the proposed framework are evaluated in three different experimental scenarios involving spring arrays and an individual with cerebral palsy (CP).
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".