Personalized Variable Gain Control With Tremor Attenuation for Robot Teleoperation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Teleoperated robot systems are able to support humans to accomplish their tasks in many applications. However, the performance of teleoperation largely depends on motor functionality and human operator's skill, especially when a human operator is short of skill training. In order to adapt to various unstructured environments for the robot system and the human operator, in this paper, a teleoperation scheme using integrated tremor attenuation with a variable gain control algorithm involving surface electromyogram is proposed to achieve personalized control performance and to reduce reliance on operator's skill. For attenuating tremor, a filter based on support vector machine is developed to guarantee normal operation. This filter depends on the machine learning scheme and does not rely on a priori filter parameters. Semiphysical experiments have been performed to demonstrate the effectiveness of the proposed methods.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it