Neural-network approach to determine operator hand orientation for teleoperated control of a robot manipulator
Bibliographic record
Abstract
In robot-manipulator teleoperation, vision-based tracking of the human operator motion offers a non-contacting approach that permits unhindered operator motion. To control the robot manipulator, the three-dimensional (3D) position and orientation of the arm of the operator is required. This paper presents a neural-network (NN) based method of determining the orientation of the human hand using non-invasive markerless vision-based tracking. The tracking method uses images of the hand from two fixed cameras to determine three angles of hand orientation. The neural network processing to determine the hand orientation consists of five procedures. First, a preprocessing system performs basic transformations on the input images to prepare them to be interpreted by the neural network. Secondly, an unsupervised neural network extracts relevant local features necessary to recognize the input patterns. Thirdly, a self-organizing neural network combines the local features of the previous network to identify the global pattern. Next, a modified radial-basis function (RBF) neural network calculates the probabilities that a given input pattern corresponds to each basic pattern, for which the RBF NN was trained. Finally, the orientation of the hand is interpolated between these basic patterns by calculating the weighted average of the most probable configurations identified by the RBF NN.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".