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Bibliographic record
Abstract
We present a simple sign language for teleassistance inspired by the work of the Bernstein (1967) and by psychophysical evidence in hand-eye coordination. In our schema, a teleoperator uses hand signs to guide an otherwise autonomous robot manipulator through a given task. Each sign signals a context switch and provides a hand-centered reference frame for the robot's servomotor routines. The signs are natural, such as pointing to an object to indicate the desire to reach toward it as well as the axis along which to reach. These signs are called deictic from the Greek word for pointing to stress their indicative and relative nature. The task example is opening a door using a Utah/MIT hand mounted on a Puma 760 arm. The teleoperator wears an EXOS hand master and polhemus sensor. Three variations of nearest neighbor pattern classification are tested for online recognition of the sign language. The simplest, in which the operator signs each pose once before starting, is the best for this task. The dual-control strategy of teleassistance combines teleoperation and autonomous servo control to their advantage. The use of a symbolic sign language helps to alleviate many problems inherent to literal master/slave teleoperation. Conversely, the integration of global operator guidance and hand-centered coordinate frames permits the servo routines to position the robot in relative coordinates and interpret feedback within a constrained context, significantly simplifying the computation and reducing the need for detailed task models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
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 it