On the Feasibility of Multi-Degree-of-Freedom Haptic Devices Using Passive Actuators
Bibliographic record
Abstract
Stability and transparency are key design requirements in haptic devices. Transparency can be significantly improved by replacing conventional electric motors with passive actuators such as brakes or dampers. Passive actuators can display a wide range of impedance and since they can only dis-sipate energy, stability is guaranteed. However, passive haptic devices suffer from a serious drawback; the direction of the force output is difficult to control. This issue was addressed extensively for planar manipulators but devices with higher degrees-of-freedom (DOF) have not been examined. In this paper, we introduce a new analytical framework to evaluate the feasibility and performance of non-redundant passive haptic manipulators with any DOF. The method identifies different regions in the workspace where a force can be created or approximated, and regions where a passive system cannot create force at all for a given user input. The results indicate that the range of forces a passive device can display increases with the number of DOF. This framework can aid in the design of control methods for multi-DOF passive 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".