Adaptive distributed fetching and retrieval of goods by a swarm-bot
Bibliographic record
Abstract
Swarm robotics is a rising paradigm which aims at designing new robot artifacts by extracting engineering guidelines from Nature. The work presented here shows the use of a particular swarm of robots called swarm-bot for carrying out distributed missions of fetching and retrieval of objects. To solve this task, a high level description plan defined in terms of behaviors is synthesized. A mission is divided in four different stages: searching for a target, calling for a swarm to aggregate as soon as one is found, jointly fetching it, and jointly retrieving it back. All robots used (s-bots) are assumed to know the same set of behaviors as well as the same behavioral plan for carrying out the task. Units are kept purely reactive, thus they do not keep any memory of their previous history. This allows to withstand changes in a highly dynamic environment. Coordination is achieved asynchronously by using light signals, whereas cooperation for the actual transportation is realized by using a force sensor located between the turret and the tracks of each s-bot. A swarm-bot, which is formed by a group of s-bots physically connected to their target, is capable of behaving during its homeward motion as if it were a single entity. Experiments show the high level of adaptability and resilience of a swarm-bot with respect to occasional possible failures of its members
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".