A local approach to developing grounded spatial references in multi-robot systems
Bibliographic record
Abstract
For a mobile robot to be able to communicate usefully with others, the symbols it uses to communicate must be grounded to entities in the environment, and those groundings made consistent among agents. While it is common practice to hand-construct such groundings, this does not scale to large problems. In particular, when communicating about useful spatial references, there are a large number of potentially relevant groundings, even for a basic task such as navigation. This paper describes the development and evaluation of an approach that allows a group of robotic agents to develop consistent shared groundings for locations in an environment over time. This approach is based on local communication and interaction, and does not rely on the ability to broadcast references to all agents, and so is suitable for domains in which communication may be sporadic, such as robotic rescue. The evaluation of this approach, which compares several different grounding techniques, shows that shared groundings can be developed effectively over time, and that these improve the effectiveness of communication in a multi-robot setting.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".