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egoengine-repro-artifacts
introvoyz042/egoengine-repro-artifacts
egoengine-repro-artifacts is a machine learning model from introvoyz042. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Large artifacts from the independent, from-scratch reproduction of EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations (arXiv:2606.12604; no official code exists).
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Public
Updated Sep 7, 2026
Repo size
505 MB
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.pt282 MB · 55%
At a glance
- License
- mit
- Access
- Public
- Created
- Sep 7, 2026
- Updated
- Sep 7, 2026
- SHA
- 7063d1f9
Model card
From the Hugging Face model README
egoengine-repro-artifacts
Large artifacts from the independent, from-scratch reproduction of EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations (arXiv:2606.12604; no official code exists).
Code + docs + small result artifacts: https://github.com/physercoe/egoengine-repro
Folders:
policy-checkpoints/— final Appendix-D flow-matching policy checkpoints (best.pt,last.pt, TorchScript exportbest_ts.pt) + normalization stats (stats.npz,dataset_meta.json). ~235 MB.retarget-outputs/— mink-IK-retargeted bimanual motions for all 16 TACO demonstration sequences (trajectory_kinematic.npzper sequence +retarget_report.json+ error/QA plots). ~201 MB.rl-checkpoints/— residual-PPO checkpoints, tensorboard logs andeval_residual.jsonresults for the escalated refine chunks (local + remote A800 batch runs). ~51 MB.
Each folder has its own README with provenance (scripts, configs) and license notes. See the GitHub repo README for headline results and honest limitations (sim-only, thin-data policy proof, RL stage under budget).
- License
- mit
- Created
- Sep 7, 2026
- Updated
- Sep 7, 2026