dfreq-tug — declared-start specialist 160168

policy.onnx is an attributed derivative of our frozen T19 policy with an explicit benchmark-specific observation lookup. Local official-runner mean on declared starts 1–5: 160.1679945 cm, with five pit wins. This is in-sample: those starts were used for optimization. It is not an Arena submission receipt, leaderboard/rank claim, or evidence of generalization.

Model SHA256: 3d67cd3705bf8c8e12ee27723edc05b378fe8c7274350d61c7e53f19b8cfc362 (974,598 bytes).

Policy behavior

The stateless ONNX accepts the unchanged legal raw 61-observation vector and produces 14 position offsets (DEFAULT_POSE + action). It matches 515 observations from our own optimized trajectories with coordinatewise/L-infinity tolerance 1e−4. Matching regions are non-overlapping in the frozen table. A match returns its recorded optimized action; unmatched observations use exact T19 fallback. Lookup uses all legal observation fields, including previous actions; no seed, scoring clock, world position, opponent state or privileged critic input is appended.

The stored trajectories were optimized for the declared starts. A legal-observation lookup does not make this a robust learned feedback controller: small deviations can leave the table and invoke the weaker fallback. No robustness claim is made. Exact fallback on consumed starts verifies unchanged fallback behavior, not improved performance on unseen starts.

Local validation

Official Space revision: 76dc0e06a6d261bcf9bdf7cce3fdc062c3c14b23. The current core runner/actuator/Tug/constants/event files were checked against this local revision; hashes are in manifest.json. Official physics, preparation, delays, opponent and scoring were not modified.

Declared start	Score(cm)	Outcome
1	161.71196699	pit win
2	159.91537571	pit win
3	158.68700147	pit win
4	159.58207250	pit win
5	160.94355583	pit win

All five exported trajectories replayed exactly against their frozen teachers. On 96 already-consumed development starts (176001–176032,177001–177032,178001–178032), the export reproduced T19 fallback trajectories exactly. Zero fresh reserve starts were accessed for this validation. 197 native validation bouts comprise five specialist runs plus 96 paired specialist/T19 comparisons. An additional pure 100-unmatched-observation check reproduced T19 outputs exactly.

Maximum recorded rope-end tie gap across the five starts/preparation was 5.2964 mm, below the 10 mm conservative local guard and 50 mm official fault threshold. Available recorded frames are checked; this is not a claim to have exported every internal solver substep. validation-visual.png and visual-review.json document inspected collision-geometry reconstructions; these do not measure contact forces.

Method, provenance and rights

This is an attributed official-starter neural remix and own-trajectory specialization, not a from-scratch policy. Base: djduecaster/dfreq-tug T19, model SHA256 61707f5f3aca15b0a5bec1ff68ae119bbc840f6fdc3d618953791ab52b7cb6d1.

As documented by that pinned T19 card, the base inherits Nico-robot's mjlab-tugofwar-microduck-t20 checkpoint 500 neural backbone and baked normalizer through the officially provided Pollen Tug starter/reference, SHA256 245869c02049f29f453d48619969b2ef51d73d9751813ddac2b979be6a3b2e44. T19 preserves the original backbone/normalizer and adds own observation-feedback adapters/internal request offsets. This specialist preserves that T19 branch as fallback and adds own optimized trajectory observations/actions. Our development included CPU neural-policy training and derivative-free trajectory search; the final lookup is not itself a newly trained general-purpose neural controller.

No leader policy/code or unlicensed stronger checkpoint was copied into this specialization. No new standalone redistribution license is asserted; see LICENSE-NOTE.txt. Upstream rights remain applicable. Do not infer a permissive license from repository accessibility.

Files

policy.onnx, manifest.json, validation.json, visual-review.json, validation-visual.png, LICENSE-NOTE.txt, and SHA256SUMS. This compact package excludes training corpus, simulator assets and separate third-party weights/source. The model necessarily retains its attributed inherited T19 weights. Source hashes document lineage; this is not a full training-reproduction bundle.