An intelligent vision guided telerobotic system for file manipulation and office automation
Bibliographic record
Abstract
Describes a vision-guided telerobotic system that enables people with disabilities to perform clerical or office tasks. By adding a light-duty robot to the office workspace, the operator can manipulate files and perform other work-related tasks. To increase the effectiveness of the robot, vision can be used to verify that the robot is correctly positioned. In addition, vision can be be coupled with the telerobotic system to allow the user more intuitive control over the robot. Visual servoing and traditional computed kinematics actions are inappropriate for this application because visual servoing requires an excessive number of iterations and computed kinematics requires accurate calibration. To counteract these difficulties and to provide user functionality, we have designed a hybrid computed-kinematics telerobotic system with an initial coarsely-calibrated computed-kinematics step followed by a more accurate visual-servoing step. We show that there are significant performance benefits from this approach. Finally, we describe how the hybrid system may be utilized in an office environment.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".