Collaborator: A Nonholonomic Multiagent Team for Tasks in a Dynamic Environment
Why this work is in the frame
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Bibliographic record
Abstract
In our previous work, we proposed a potential field-based hybrid path planning scheme for robot navigation that achieves complete coverage in various tasks. This paper is an extension of this work producing a multiagent framework, Collaborator, that integrates a high-level negotiation-based task allocation protocol with a low-level path planning method taking into consideration several real-world robot limitations such as nonholonomic constraints. Specifically, the proposed framework focuses on a class of complex motion planning problems in which robots need to cover the whole workspace, coordinate the accomplishment of a task, and dynamically change their roles to best fit the task. Applications in this class of problems include bomb detection and removal as well as rescuing of survivors from accidents or disasters. We have tested the framework in simulations of several tasks and have shown that Collaborator can satisfy nonholonomic constraints, cooperatively accomplish given tasks in an initially unknown dynamic environment while avoiding collision with other team members. Finally we prove that the proposed control laws are stable using the Lyapunov stability theory.
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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 | 0.000 |
| 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 it