BlinkMimic：単一GPUで数分間にヒューマノイド動作トラッカーを学習
BlinkMimic: Training a Humanoid Motion Tracker in Minutes on a Single GPU
並列シミュレーションから学んだワールドモデルを通じた誤差逆伝播とMuon更新を基に、学習率減衰・失敗時リセット・GPUグラフキャプチャを加え、Unitree G1の40動作を単一RTX 4090で3.4分で学習する手法を提案。
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著者: Fuyi Chen
分類: cs.RO, cs.LG
原文アブストラクト
Whole-body motion tracking lets a humanoid robot follow reference motions, but training a tracking policy for a library of motions has usually taken hours of GPU time. SuperMimic showed that the policy can instead be trained by backpropagating through a world model learned from parallel simulation, and InstantTrack added Muon updates, a residual policy around the reference and an averaged deployed policy. We build on both and present BlinkMimic, a recipe that makes this training faster. On a simulated Unitree G1, BlinkMimic trains one policy for all 40 LAFAN1 clips in 3.4 minutes on a single RTX 4090, measured until three consecutive checkpoints reach a normalized return of at least 0.82 with no failed clip (mean of three seeds). We split training time into the cost of an iteration, the iterations needed to fit the motions, and the iterations needed to remove the last failures, and assign each component, inherited or new, to one of these terms. The new components are a step size that decreases once training has progressed, resets directed by failures of the deployed policy under deployment conditions, and GPU graph capture of the whole iteration. A leave-one-out ablation with three seeds per component shows which term each component affects, and further measurements of the gradient, the update rule and the failure cases explain these effects. Code and per-seed results are available at https://github.com/fuyichen0725/blinkmimic.