Sample-Efficient Policy Adaptation for Exoskeletons Under Variations in the Users and the Environment
Bibliographic record
Abstract
Controlling lower-limb exoskeletons is extremely challenging due to their direct physical interaction with users wearing them which imposes additional safety concerns. Furthermore, the control policy needs to adapt for different users and surfaces the robot is traversing. Hence, it is crucial to design a control framework that can perform robustly in the presence of these variations. In this letter, we propose a sample-efficient method based on Bayesian Optimization (BO) to adapt a model-based walking controller for a lower-limb exoskeleton, XoMotion. In order to mitigate safety risks, we use a set of dummy weights with realistic inertial distributions in the experiments with the robot to find optimal policies. An extensive set of experimental results shows that the proposed controller can successfully adapt for different users and different terrains, in less than 15 real-world trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".