A High-Bandwidth Back-Drivable Hydrostatic Power Distribution System for Exoskeletons Based on Magnetorheological Clutches
Bibliographic record
Abstract
Exoskeletons are increasingly interesting for human assistance applications ranging from rehabilitation to force enhancement. However, today's exoskeletons are relatively slow and lack the mechanical transparency required to complete several daily tasks, mainly due to their bulky and non-back-drivable actuation mechanisms. To improve upon conventional exoskeleton designs, this letter presents a novel power-distribution system that combines magnetorheological (MR) clutches and low-friction hydrostatic transmissions using rolling diaphragms. In such a system, MR clutches are used to rapidly modulate the torque provided from a centralized power source and distribute it to each joint through a high-bandwidth, back-drivable, and low-inertia transmission. The main objective of this letter is to investigate the transparency performance of the MR-hydrostatic power distribution in terms of its force-bandwidth and back-drivability with the aim of being used in future exoskeletons. Experiments with a custom one degree-of-freedom haptic joint are supported by an analytical model that demonstrates the high bandwidth (>40 Hz) and good backdrivability (2-11% of peak force) of an MR-hydrostatic system.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".