COGENT: Co-design of Robots with Generative Flow Networks
Why this work is in the frame
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Bibliographic record
Abstract
Co-design of robots involves optimizing the control mechanism and physical form together. This intertwined design process is inherently challenging and sample inefficient because of the large design and control search spaces. We introduce COGENT, a novel framework that leverages a graph synthesis technique named GFlowNet, to enhance search space traversal in robotic co-design. To increase sample efficiency, the proposed framework introduces a cost/performance-aware design prioritization mechanism that learns a design generator policy by carefully sampling the design space. Our experiments show the effectiveness of the proposed framework in various robot co-design tasks. Evaluations performed on a wide range of agent design problems demonstrate that our method significantly outperforms baselines. We show that COGENT produces a suite of diverse designs achieving better task objectives across all evaluated design problems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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