Vision-Based Hand Gesture Recognition With Deep Machine Learning for Visual Servoing
Bibliographic record
Abstract
Vision based robot motion control (Visual Servoing) is a challenging research direction in utilizing vision information to control the robot motion. In this work, we successfully controlled the real-time pose of an end-effector based on a hand gesture detector that we trained with acquired training data in the lab environment. Meanwhile, a machine learning model for hand language translation based on convolutional neural network is proposed and utilized in this paper. SSD is the suggested meta-architecture that uses single feed-forward convolutional network for straightly predicting categories. The proposed model is evaluated on Tensorflow platform along with Pascal VOC 2012. In addition, an image-based vision servoing system based on Lyapunov’s theory is developed to control velocities of the robot’s joints. In the experimentation, the integration of the above systems and MobileNet network as a convolutional feature extractor shows good performance in identification, tracking and motion control of the robot. The model achieved 98% mAP and 0.8 for Total-loss while visual servoing also demonstrated good performance during experimentation.
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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 | 0.000 | 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 | 0.000 |
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".