Assist-as-Needed Framework for Robotic Rehabilitation: Adaptive Admittance Control With Passivity-Based Safety Features
Bibliographic record
Abstract
This paper presents an adaptive admittance control scheme that integrates an adaptive neural network (NN) algorithm as a shared autonomy framework to achieve the Assist-as-needed (AAN) property in robotic rehabilitation. The proposed algorithm enables real-time adjustment of control parameters based on human performance, without requiring extensive offline training. An energy-based performance index dynamically balances tracking accuracy with minimal robotic intervention to encourage active human participation. Furthermore, a modified virtual energy tank approach is introduced to preserve system passivity, preventing unsafe behaviors. Experimental results underscore the algorithm’s adaptability, ensuring compliant behavior as evidenced by a notable 83% reduction in average stiffness, reflecting a corresponding decrease in robotic intervention, due to detection of active human participation. Moreover, the algorithm ensures safe interaction and effective task completion. These findings highlight the framework’s potential for improving robotic rehabilitation by intelligently adapting to user needs and providing safety-aware control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".