Characterizing device dynamics for haptic manipulation and navigation
Bibliographic record
Abstract
In this work we present a method of systematically selecting regions of a haptic workspace to be used for navigation of large virtual environments. Existing navigational techniques require the partitioning of the workspace into a region of manipulation and a separate region for navigation tasks. These techniques, however, have neglected to describe an effective way to implement these concepts in a device specific manner. We propose a two step technique to define these regions based on the mechanical properties of pre-existing devices. In the first step, the kinematic properties of the device are analyzed across the entirety of the physical workspace. A well-behaved region that favours isotropic mapping from the joint actuators to the generalized forces at the end effector is selected. Having ensured high force fidelity within this region, we then perform a second step analyzing the device dynamic properties. We further subdivide the navigational space into a region with suitable inertial properties in which manipulation tasks may be performed. This procedure generates a haptic display with a highly transparent haptic manipulation region within which a user can interact with a virtual environment. This manipulation region is bounded by a well-behaved navigational region that ensures adequate force transmission. To demonstrate this technique, navigation and manipulation spaces are generated and described for the planar and spatial cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".