Model-Based Programming of Modular Robots
Bibliographic record
Abstract
Modular robots are a powerful concept for robotics. A modular robot consists of many individual modules so it can adjust its configuration to the problem. However, the fact that a modular robot consists of many individual modules makes it a highly distributed, highly concurrent real-time system, which are notoriously hard to program. In this work, we present our programming framework for writing control applications for modular robots. The framework includes a toolset that allows a model-based programming approach for control application of modular robots with code generation and verification. The framework is characterized by the following three features. First, it provides a complex programming model that is based on standard finite state machines extended in syntax and semantics to support communication, variables, and actions. Second, the framework provides compositionality at the hardware and at the software level and allows building the modular robot and its control application from small building blocks. And third, the framework supports formal verification of the control application to aid the gait and task developer in identifying problems and bugs before the deployment and testing on the physical robot.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".