低頻度VLMを高頻度制御に活用するLeap-VLA
Leap-VLA: High-Rate Robot Control from a Low-Rate VLM Backbone
VLMの意味推論を低頻度で走らせキャッシュを再利用し、高頻度のフローマッチング行動エキスパートでロボットを制御するVLAを提案。キャッシュの古さを考慮した訓練で非同期制御の性能劣化を大幅に抑えた。
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著者: Lin Yang, Yunjie Shen, Zheyu Li, Zhenjie Tian, Yongli Wang, Dongze Huang, Weiqi Wang, Yucheng Zhu, Zhaoquan Jiang, Guofeng Xie, Peigen Fang
分類: cs.AI
原文アブストラクト
Vision-language-action (VLA) models re-encode the full observation through a multi-billion-parameter vision-language model (VLM) at every control step, even though scene semantics evolve at roughly 1 Hz while reactive control requires 20-50 Hz. We present a VLA that decouples the two: a VLM runs as a low-frequency semantic anchor whose cached context is reused across control steps, while a flow-matching action expert operates at high frequency, consuming the cached semantics with a fresh, low-latency visual stream from a compact encoder pretrained on robot video with masked reconstruction, optical flow, and depth. The measured age $τ$ of the cache conditions the expert through adaptive layer normalization. Reusing the cache makes the policy a function of its age, and training covers that function explicitly: cache ages sampled from real episode history, plus an age-ramped pixel corruption that erases the stale snapshot the way deployed staleness corrupts content. On the asynchronous deployment stack, the recipe closes most of the staleness gap: on LIBERO-Spatial, 61.4% to 95.5% at a 2 s semantic refresh (95.9% at 1 s, within 1.6 points of the frequency-matched anchor); averaged over the four LIBERO suites, the deployed policy holds 95.2% at a 1 s refresh and 94.5% at 2 s against a 96.3% matched anchor. A forward-only chunk-boundary correction (soft-nudge), an autograd-free reformulation of real-time-chunking guidance, carries per-control-step re-planning without loss (97.1%) at zero added latency. The same lens exposes a second defect: under an instruction-scene mismatch the policy keeps manipulating the scene, and a chain-of-thought verdict gate trained on zero-cost negative episodes freezes execution exactly on mismatched instructions without hurting feasible success.