AnyStep-WAM: 予算整合蒸留と適応推論によるワールドアクションモデル
AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models
ワールドアクションモデルのノイズ除去ステップ数をタスクの難易度に応じて適応的に配分し、成功率を維持しつつ計算量を最大85%削減するフレームワークを提案。
詳しい要約
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著者: Rui Wang, Xiangyu Wang, Donglin Yang, Yibo Li, Canyang Chen, Zhongrui Wang, Xiaojuan Qi
分類: cs.RO
原文アブストラクト
World-action models (WAMs) couple predictive visual modeling with action generation, typically relying on iterative denoising with a fixed denoising steps. However, manipulation tasks contain actions chunks with varying sensitivity to generation errors: critical actions require precision, while less sensitive actions allow faster generation with fewer denoising steps. Here we introduce AnyStep World Action Model, a general framework for tunable-budget prediction and scene-dependent computation allocation. Our budget-aligned teacher-trajectory distillation trains interval-conditioned flow maps using explicit frozen-teacher transitions and shared low-rank adapters, supporting action generation from one-step prediction to multi-step refinement. Building on this capability, a lightweight risk-benefit scheduler predicts teacher-curvature-based difficulty and budget-specific student-teacher fidelity from a single one-step preview, selecting the smallest budget predicted to satisfy risk-adaptive fidelity requirements. We evaluate our framework on three widely used WAMs Motus, FastWAM, and LingBotVA using RoboTwin 2.0. Our method reduces average denoising steps by 60.2%, 49.8%, and 85.28%, respectively, while maintaining baseline task success rates. In particular, our AnyStep training substantially improves model performance under a one-step denoising budget, increasing task success rates by 7.07%, 12.08%, and 8.94% on Motus, FastWAM, and LingBotVA, respectively. Experiments on six real-world manipulation tasks further validate its effectiveness.
