Shaft Compliance as a Soft Sensor to Eliminate Stiction in Hybrid Haptic Devices
Bibliographic record
Abstract
Haptic devices that rely on electric motors to generate force feedback suffer from an inherent trade-off between performance and stability. A newer class of devices use hybrid actuation with brakes and motors to improve force rendering. However, while brakes are intrinsically stable, they can only oppose an input torque. When the brake and motor are activated at the same time, the brake blocks both the motor’s and the user’s input torque and can create stiction - an unwanted resistance to motion that negatively affects simulation realism. In this paper, we propose the use of shaft compliance as a soft torque sensor in a hybrid actuator integrating a DC motor and a brake. The angular displacement of the compliant element is used in a controller to infer the user’s input torque from readings of a single position encoder. The controller shares a desired torque between each actuator while preventing stiction. Experimental evaluation of the proposed sensor confirms the sensor’s ability to detect torque and the controller’s ability to simulate virtual walls, despite sensor accuracy limitations and nonlinearities. The proposed sensor concept offers a reliable and low-cost alternative for torque sensors in portable haptic devices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".