幾何学的潜在拡散による空間認識ワールドアクションモデル
Spatially Aware World Action Model via Geometric Latent Diffusion
事前学習済みビデオ拡散モデルをRGBと深度の同時予測に拡張し、3D情報を活用したロボットポリシー学習を実現する空間認識ワールドアクションモデル(SA-WAM)を提案した。
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著者: Javier Alejandro Lopetegui Gonzalez, Paul Pacaud, Cordelia Schmid
分類: cs.CV, cs.RO
原文アブストラクト
World Action Models (WAMs) leverage the capabilities of large-scale pretrained video diffusion models to jointly predict future observations and actions, inheriting rich visual and physical priors from internet-scale video. This has made them a promising paradigm for robot policy learning, yet the prevailing models operate exclusively on RGB observations and do not leverage 3D information. To bridge this gap, we introduce a Spatially Aware World Action Model (SA-WAM), which repurposes a pretrained video model for joint action, RGB, and depth prediction, enabling 3D-aware world modeling and action prediction within a single diffusion backbone. We use a nonlinear encoding that maps the unbounded depth signal into the bounded input domain expected by the frozen VAE tokenizer. This allows us to reuse the tokenizer without 3D-specific fine-tuning, incorporating geometric information without sacrificing the pretrained priors. SA-WAM achieves state-of-the-art results on the RoboCasa and LIBERO-Plus benchmarks, while simultaneously improving future-state predictions. Furthermore, SA-WAM outperforms strong baselines in real-world evaluation using a UR5 robotic arm, with strong gains in randomized environments. We analyze the correlation between world model prediction quality and rollout success, providing insights into WAM performance and avenues for its improvement.