Biologically Inspired Collective Robotics
Bibliographic record
Abstract
In this chapter, we review our recent research in the area of collective robotics, and the problem of controlling multiple robots in the completion of common tasks. Our approach is characterized with a strong inclination for biological inspiration in which examples in nature — social insects in particular — are used as a way of designing strategies for controlling robots. This approach has been successfully applied to the study of three representative tasks, namely, collective box-pushing, collective construction, and collective sorting. Collective box-pushing deals with the purposeful motion of an object too large to be moved by a single robot and we rely on the group prey transport phenomenon found in ants to derive the necessary behaviors for accomplishing this task. Collective construction is concerned with the building of a geometric structure with the combined efforts of many individuals in parallel, without centralized control and we study a species of ant known to possess this capability, to model and control the process of creating a circular nest with multiple robots. Finally, in collective sorting the broad behavior in ants serves as the motivation behind designing robotic behaviors that depend on only local sensing in clustering objects of different types into separate piles. The success of our proposed approach is supported by both simulation and physical experiments using robots.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".