Learning and robotic imitation of therapist's motion and force for post-disability rehabilitation
Bibliographic record
Abstract
The inclusion of robots in therapy is becoming common due to robots' power, repetitive motion ability, reprogramming capacity and adaptability to new tasks. In recent years, the demand for rehabilitation services has increased due to the rising number of patients with disability. In this paper, we propose a solution to the rising demand for therapists' services by combining Learning from Demonstration (LfD) and robotic rehabilitation. The goal of the paper is to implement LfD to model and learn the therapist's behavior (be it a trajectory or force) as a nonlinear dynamic system using a method called Stable Estimator of Dynamical Systems (SEDS) to later reproduce the learned behavior in the absence of the therapist using a robot. This method allows the therapists to first train a robot to learn his/her behavior such that, later when the therapist is no longer involved and the patient works alone with the robot, the robotic system determines whether and how to interact with the patient the same way the therapist would have interacted.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".