Learning User-Specific Control Policies for Lower-Limb Exoskeletons Using Gaussian Process Regression
Bibliographic record
Abstract
Robotic exoskeletons provide a viable means for enabling individuals with limited or no walking ability to traverse various surfaces with maximal external support to the patient’s body. However, to achieve effective performance, it is crucial to consider anatomical differences in body size and shape among users. In this paper, we propose a framework to infer adapted user-specific policies using a small dataset from past experiments performed with twelve users wearing a lower-limb self-balancing exoskeleton. Our framework utilizes Gaussian Process Regression (GPR) to learn a mapping between user characteristics and control policy parameters. We also propose to use hindsight data relabeling to improve the performance of the controller. We experimentally test the output of the GPR model on new users and demonstrate its effectiveness in predicting user-specific walking parameters that lead to high performance. We also compare the performance of this control policy with an expert-tuned policy and show that our framework can reach comparable results without the need to perform expensive and unsafe tuning of the controller for new users.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".