StreamVLA: 完了状態ゲーティングによる推論と行動のサイクル分離
StreamVLA: Breaking the Reason-Act Cycle via Completion-State Gating
高レベル推論と低レベル制御を二重システムで統合し、サブタスク遷移時のみ推論を発火させることで、遅延を48%削減しつつLIBEROで98.5%の成功率を達成したVLAモデル。
詳しい要約
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著者: Tongqing Chen, Hang Wu, Jiasen Wang, Xiaotao Li, Lu Fang
分類: cs.RO
原文アブストラクト
Long-horizon robotic manipulation requires bridging the gap between high-level planning (System 2) and low-level control (System 1). Current Vision-Language-Action (VLA) models often entangle these processes, performing redundant multimodal reasoning at every timestep, which leads to high latency and goal instability. To address this, we present StreamVLA, a dual-system architecture that unifies textual task decomposition, visual goal imagination, and continuous action generation within a single parameter-efficient backbone. We introduce a "Lock-and-Gated" mechanism to intelligently modulate computation: only when a sub-task transition is detected, the model triggers slow thinking to generate a textual instruction and imagines the specific visual completion state, rather than generic future frames. Crucially, this completion state serves as a time-invariant goal anchor, making the policy robust to execution speed variations. During steady execution, these high-level intents are locked to condition a Flow Matching action head, allowing the model to bypass expensive autoregressive decoding for 72% of timesteps. This hierarchical abstraction ensures sub-goal focus while significantly reducing inference latency. Extensive evaluations demonstrate that StreamVLA achieves state-of-the-art performance, with a 98.5% success rate on the LIBERO benchmark and robust recovery in real-world interference scenarios, achieving a 48% reduction in latency compared to full-reasoning baselines.
