Vector Field Driven Design for Lightweight Signal Processing and Control Schemes for Autonomous Robotic Navigation
Bibliographic record
Abstract
We address the problem of realizing lightweight signal processing and control architectures for agents in multirobot systems. Motivated by the promising results of neuromorphic engineering which suggest the efficacy of analog as an implementation substrate for computation, we present the design of an analog-amenable signal processing scheme. We use control and dynamical systems theory both as a description language and as a synthesis toolset to rigorously develop our computational machinery; these mechanisms are mated with structural insights from behavior-based robotics to compose overall algorithmic architectures. Our perspective is that robotic behaviors consist of actions taken by an agent to cause its sensory perception of the environment to evolve in a desired manner. To provide an intuitive aid for designing these behavioral primitives we present a novel visual tool, inspired vector field design, that helps the designer to exploit the dynamics of the environment. We present simulation results and animation videos to demonstrate the signal processing and control architecture in action.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".