Instructions to use djduecaster/dfreq-rollers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Microduck
How to use djduecaster/dfreq-rollers with Microduck:
# Replace SLOT with the slot specified in the model card (walk, stand, sitstand, ground_pick, kick_left, kick_right, roulade). sudo robotctl policy load SLOT djduecaster/dfreq-rollers
- Notebooks
- Google Colab
- Kaggle
File size: 1,481 Bytes
1f132ad | 1 2 3 4 5 6 7 8 | # Private Roller selection: 2.570 seconds
The own original N8 policy averaged2.715s on the five declared starts. A640-bout own seed3 trajectory search yielded2.610s; transferring its fixed parameters onto all five own reference trajectories yielded2.652s. A further1600-bout continuation yielded2.525s on seed3. Transferring this continuation improved seeds1/3/4/5 but caused a forward fall onseed2 at1.76s. We therefore retain the earlier successfulseed2 trajectory; the failed continuation is preserved, not concealed.
Selected times [2.565,2.680,2.525,2.535,2.545], mean2.570s, official full command sweep5/5finish. Model combined-2570.onnx SHA25679019a39d0e3d314db2b6a90c31f5575b70ccf6b235bc3acee5163aeaeedd61c. Exact native replay verified on all five selected teachers; five previously consumed900001–900005 starts reproduce N8 fallback exactly. No fresh-start generalization claim. Selection and optimization are in-sample on declared benchmark starts. The stateless61-observation lookup export preserves original N8 fallback; simulator physics and interface unchanged.
Visual review notes increased lateral drift on several starts, particularlyseed5 to18.94cm. Faster benchmark completion is not evidence of robust general-purpose locomotion. No public submission or rank claim. Private backup includes original baseline/first/shared models, failure captures, completed continuation state and validation receipts; it is not a complete standalone training environment.
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