An Intent-Preserving Approach to Telerobotic Rehabilitation
Bibliographic record
Abstract
Often the design of control systems for telerobotic rehabilitation focuses on maintaining stability and maximizing transparency, but does not consider the intent of therapeutic interactions. When therapists assist patients during therapy activities, they tailor the amount of assistance and guidance delivered so that the patient engages fully in the therapy. Existing telerobotic control systems deliver the delayed therapist force to the patient, making only minimal changes as necessary to maintain stability. However, communication time delays can distort the effect of the therapist force and lead to remote interactions that do not reflect the therapist’s intent, such as over- or under-assisting the patient. In this work, we propose a method for identifying the therapist’s intent from their applied force and the velocity of the patient. Using this understanding of intent, we propose two approaches to ensure that the therapist’s intent is preserved across the communication channel: Rotational Intent-Preserving Teleoperation (RIPT), where the delayed force from the therapist is rotated to maintain the intended amount of assistance and guidance, and scaled-RIPT, where less relevant forces are reduced. We test these approaches in simulations, finding that they can prevent unintended over-assistance from the therapist.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".