Vision-Based Localization and Tracking of Objects Through Robotic Manipulation
Bibliographic record
Abstract
Among all the technological developments in the past decade, innovations in robotics are one of the most significant. Robots can now carry out both simple and complex jobs from laboratory to industrial settings with accuracy and efficiency. With the help of vision, robotics and AI offer humans numerous opportunities. This research aims to illustrate the development of a vision-based robot manipulation system that can locate and track a target object in real time. The system employs a depth camera, a UFactory xArm robot, a pre-trained model, and OpenCV to receive vision sensory input, recognize objects, and generate interactive coordinates for each target object. Using a 5-degree-of-freedom robot (xArm-5) and a RealSense depth camera, a thorough experiment was conducted to validate the proposed system’s performance. Using the detection accuracy of 75.2% and average depth accuracy of 94.5%, the proposed system performs stably and can successfully track target objects via robot manipulation with 30 frames per second. This technology has tremendous promise in the fields of exploration, mobile robots, and assistive robotic systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".