UniviewVLA：世界モデリングを備えた統合マルチビュー視覚言語行動モデル
UniviewVLA: A Unified Multiview Vision-Language-Action Model with World Modeling
標準的な2つのカメラ観測のみから、世界モデルを用いてマルチビューの未来シーンを生成し、遮蔽された情報を補完して行動予測を改善するVLAモデルを提案。推論高速化のためのトークン圧縮と、動的な視点選択手法も導入。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Tao Xu, Runhao Zhang, Zhijian Huang, Jiayi Guan, Jiaxin Wang, Yifan Ding, Yong-Lu Li, Long Chen, Guang Chen, Jinghui Lu
分類: cs.RO
原文アブストラクト
Occluded tasks remain a bottleneck in robot manipulation. Existing solutions either deploy additional physical cameras requiring training-inference camera parity, or rely on explicit 3D reconstruction with high computational cost. Moreover, both approaches rely on standard agent-view and wrist-view observations, while failing to capture occlusion information and future scene evolution. To this end, we propose UniviewVLA, a unified multiview Vision-Language-Action model with world modeling, which infers multiview scene evolution for action prediction from only standard two-camera observations. We demonstrate that by leveraging generated multiview future views from the world model, UniviewVLA reveals occluded cues and models future scene evolution, improving action prediction and removing the need for extra hardware or explicit reconstruction. Besides, to accelerate inference while preserving prediction accuracy, UniviewVLA develops Motion-Informative Token Compression, which compresses each generated view from 625 to 16 tokens and reduces per-view latency from 6-7s to 0.2-0.3s. UniviewVLA also proposes training-free Action-Entropy View Selection, which dynamically identifies the most action-informative view at different inference stages. Extensive experiments show that UniviewVLA achieves 95.8% on LIBERO and 4.60 on CALVIN ABCD to D, both standard occlusion-free benchmarks. On customized occlusion-focused tasks, it improves success rate from 40.0% to 73.3%, and average real-robot success rate by 33.4 points, demonstrating stronger occlusion-focused performance without sacrificing standard occlusion-free benchmarks.
