A Novel Torque Sensing Approach to Eliminate Stiction in Haptic Devices with Hybrid Motor/Brake Actuation
Bibliographic record
Abstract
Hybrid haptic devices combining brakes and motors enable precise force rendering in applications spanning teleoperation to virtual reality simulation. Brakes can provide a strong resistance to motion, while motors can simulate interactions with elastic or animated elements. However, a common issue arises when the user pushes against an elastic element and reverses motion. Upon reversal, the actuator must apply a force in the direction of the user’s motion. Since brakes can only oppose a force, they instead resist the user, creating an unwanted resistance known as stiction. Solutions to this problem often involve bulky rotary torque sensors or imprecise strain gauges to detect the user-applied force and deactivate the brake accordingly. However, these solutions introduce substantial inertia and friction to the actuator, are expensive, and not suitable for portable devices.This paper introduces a novel torque sensing approach that embeds a miniature 1-DOF force sensor in a rotary hybrid actuator without adding any inertia or friction. A new control algorithm is proposed to determine an optimal partition of a virtual environment torque between the brake and motor, and disengage the brake before stiction occurs. The proposed approach is validated through a series of experimental testing on a 1-DOF hybrid actuator prototype. The results show that proposed approach successfully eliminates stiction while preventing oscillations around zero velocity. Compared to conventional instrumentation methods, this implementation offers a more costeffective, compact, and reliable alternative to the design and control of hybrid 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 | 0.001 |
| 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 | 0.001 |
| 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".