Vision-Based Localization and Tracking of Objects Through Robotic Manipulation
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
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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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 it