Voice Activated Human Following Robot Using Computer Vision
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the development of a voice-activated human-following robot that integrates computer vision and sensor-based obstacle avoidance to enable autonomous navigation in dynamic environments. The system employs a Raspberry Pi 4 as the primary processing unit and a Pi Camera to perform real-time human detection using a lightweight YOLOv5 model. Voice commands are captured through a microphone and processed using speech recognition to activate or control the robot. Ultrasonic sensors are used to detect and avoid obstacles, enhancing safety and reliability. The robot successfully combines vision, voice, and proximity sensing into a low-cost, flexible platform suitable for applications such as personal assistance, industrial tool handling, and smart surveillance. Experimental results demonstrate effective human tracking, responsive voice control, and reliable navigation in indoor settings
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it