ChunkFlow: 連続性を保つチャンク型ポリシー学習に向けて
ChunkFlow: Towards Continuity-Consistent Chunked Policy Learning
VLAモデルのチャンク型アクション出力で生じる境界の不整合（ジッタ）を、チャンク構造と実行を整合させる学習・実行フレームワークChunkFlowを提案し、CALVINやLIBERO、実ロボットで成功率と安定性を改善した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Zhao Yang, Yinan Shi, Mingyuan Yao, Wenyao Xue, Yawei Jueluo, Longjun Liu
分類: cs.RO
原文アブストラクト
Vision-language action (VLA) models increasingly adopt chunked action heads to satisfy real-time constraints; however, this introduces boundary jitter: overlapping regions between consecutive chunks often yield inconsistent predictions, degrading temporal coherence and the task success rate. Existing methods, such as inference-time blending, merely reweight mismatched proposals without correcting underlying errors, leading to residual accumulation under biased or noisy histories. We propose ChunkFlow, a seam-aware training-and-execution framework for chunked policies that aligns chunk structure with boundary execution. It partitions each chunk into frozen, editable, and future zones, applies deterministic overlap blending at execution, and trains raw predictions with seam and first- and second-order continuity losses. History corruption and scheduled sampling improve robustness to executed-history errors, while an AWAC fine-tuning stage adapts the policy without removing these structural regularizers. Under mild smoothness assumptions, pre-blending seam discrepancies provably decay with increasing overlap. Experiments on CALVIN, LIBERO, and real robots show an improved success-stability trade-off with low-latency inference. Project page: https://cytoderm-ai.github.io/chunkflow.
