A Highly-Geared Haptic Actuator using 3D Printed Magnetorheological Clutches
Bibliographic record
Abstract
Abstract Advanced robotic systems such as humanoid robots need actuators with high torque density but yet, with good haptic abilities, in order to interact transparently with people. Combining these two requirements presents an important challenge for conventional gearmotors due to a gearing design conflict where gearing increases torque density but only at the cost of reduced haptic performance. Recent research suggests that magnetorheological (MR) actuators have the potential to greatly reduce the gearing design conflict by introducing a small fluidic clutch between the “gear” and the “motor” which allows maintaining excellent haptic performance at high gearing. Fully extracting the benefits of MR actuators requires pushing gearing ratios above 100:1 in combination with miniature low friction and inertia clutches which presents serious manufacturing challenges. This paper presents a manufacturing solution for such miniature, low friction and inertia, 3D printed MR clutch design and integration in an actuator with 120:1 gearing ratio. An extensive experimental characterisation is conducted on a fully-functional actuator showing excellent backdrivability and frequency response even with such high gearing levels thus opening the door to a future generation of torque-dense, but yet haptic robot actuators.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".