Adaptive distributed fetching and retrieval of goods by a swarm-bot
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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