Bio-Inspired Vision and Gesture-Based Robot-Robot Interaction for Human-Cooperative Package Delivery
Bibliographic record
Abstract
This research presents a novel bio-inspired framework for two robots interacting together for a cooperative package delivery task with a human-in the-loop. It contributes to eliminating the need for network-based robot-robot interaction in constrained environments. An individual robot is instructed to move in specific shapes with a particular orientation at a certain speed for the other robot to infer using object detection (custom YOLOv4) and depth perception. The shape is identified by calculating the area occupied by the detected polygonal route. A metric for the area's extent is calculated and empirically used to assign regions for specific shapes and gives an overall accuracy of 93.3% in simulations and 90% in a physical setup. Additionally, gestures are analyzed for their accuracy of intended direction, distance, and the target coordinates in the map. The system gives an average positional RMSE of 0.349 in simulation and 0.461 in a physical experiment. A video demonstration of the problem statement along with the simulations and experiments for real world applications has been given here and in Supplementary Material.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".