Bibliographic record
Abstract
Abstract Cobots are devices which use computer‐oriented passive constraints to guide an end‐effector driven by a human. This synergistic union of human skill and robotic precision is desired in fields such as surgical robotics (our application area of interest) where the surgeon would prefer not to hand over control of a procedure to an autonomous robot. Typical cobot designs intrinsically allow at most one degree of freedom of motion, but there are some tasks (such as using a bone saw to cut a plane in knee replacement surgery) where allowing two or more degrees of freedom is desireable. While it is possible to use selective constraint alignment to increase the apparent degrees of freedom of a cobot, this requires more actuators than are strictly necessary for the task, as well as a force sensor to detect the user's intent. We, therefore, introduce here the concept of minimally constrained cobots for multiple degree of freedom (DOF) tasks such as planar cutting, and outline a general framework for controlling such devices. We illustrate our control algorithms by using a planar cart example and discuss how they might be applied to potential designs for three‐dimensional parallel cobots intended for surgical applications. © 2002 Wiley Periodicals, Inc.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".