Human-Exoskeleton Disagreement Resolution Through Interaction Torque Minimization: Experimental Results
Bibliographic record
Abstract
This paper presents experimental validation of a recently proposed Assist-As-Needed controller which utilizes human-robot interaction force to adapt the exoskeleton's reference trajectory. Using the estimated dynamics of Indego lower-limb exoskeleton in single and double support regimes, we accurately estimate the human-exoskeleton interaction torques all over the gait cycle. This enables the exoskeleton to adapt its desired joint angle trajectories by minimizing its physical interaction with the user, thereby resolving the human-exoskeleton physical disagreement. The results of our experiments on two able-bodied participants show that the proposed reference trajectory adaptation method can boost performance metrics such as assistance efficiency and assistance coordination with user intention. Moreover, we observed reductions in muscular activity, with decreases ranging from 13% to 54%, across muscles acting on the hip or knee joints. While direct measures of gait stability were not assessed, the proposed trajectory adaptation is associated with improved foot clearance metrics, specifically minimum toe and maximum heel clearance. This improvement, coupled with a reduction in users' muscle activity, may suggest enhancements in perceived stability compared to using a fixed desired trajectory for controlling the exoskeleton.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".