USING YUMI ROBOT AND RGB-D CAMERA WITH YOLOV5 FOR PICK-AND-PLACE APPLICATION, 1-10.
Bibliographic record
Abstract
Nowadays, in many industries, robots and cameras are used together to detect certain objects and perform specific tasks.However, misdetection can be occurred due to uncertainty of lighting condition, background, and environment.Using a dual arm 7-DOF collaborative robot and RGB-D camera with YOLOv5 in pickand-place application is proposed in this research to resolve the aforementioned problems.The images are collected and labelled in preparation of the dataset.The dataset is trained with the machine learning algorithm, YOLOv5.It became weight for realtime detection.When RGB images from the camera are sent to YOLOv5, data in regard to position x -y and colour of the bottle is extracted from the depth and the colour images.The experiments were done to assess the performance of YOLO and the grasping capability of the robot.
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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".