End-to-End Deep Reinforcement Learning for Exoskeleton Control
Why this work is in the frame
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Bibliographic record
Abstract
Patient-specific control and training on lower body exoskeletons can help improve a user's gait during post-stroke rehabilitation by increasing their amount of participation and motor learning. Traditionally, adaptive control techniques have been used to provide personalization and synchronization with exoskeleton users, but they require predefined dynamics models of the user and exoskeleton. However, these models can be difficult to accurately define due to the complexity of the human-robot interaction. Most recently deep reinforcement learning techniques have shown potential to effectively learn control schemes without the need for system dynamics models. In this paper, we present for the first time an end-to-end model-free deep reinforcement learning method for an exoskeleton that can learn to follow a desired gait pattern, while considering a user's existing gait pattern and being robust to their perturbations and interactions. We demonstrate the effectiveness of our proposed method for user personalization of gait training in simulated experiments.
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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 it