長期予測のための構造化潜在ダイナミクスを持つ身体性ワールドモデル
Structured Latent Dynamics for Long-Horizon Embodied World Models
ワールドモデルの潜在状態を一般化座標と運動量からなる位相空間で表現し、散逸ハミルトン力学で遷移させることで、長期予測の安定性と物理的一貫性を向上させた研究。
詳しい要約
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著者: Kevin Chen, Yu-Wei Zhan, Haoran Liang, Sen Cui, Changshui Zhang
分類: cs.LG, cs.CV
原文アブストラクト
Reliable long-horizon prediction remains a central challenge for embodied world models. Although recent generative models can synthesize visually plausible action-conditioned futures, their latent dynamics are typically learned through unconstrained transition modules, which may gradually deviate from coherent physical evolution as predictions are recursively rolled forward. We investigate whether explicitly structuring latent state evolution can improve the stability and utility of such imagined trajectories. To this end, we introduce HWM, a structured dynamics framework that represents backbone latents in a learned phase space consisting of generalized coordinates and momenta. Future states are propagated through dissipative Hamiltonian dynamics and mapped back to the original latent representation through a learnable inverse interface. The resulting formulation can be integrated into existing world-model architectures without redesigning their perception or decoding components, while multi-step transition supervision encourages the structured rollout to remain aligned with the latent trajectories induced by real observations. Controlled simulation experiments show that HWM produces more accurate estimates of velocity, acceleration, and jerk and substantially reduces long-horizon energy drift relative to unstructured latent dynamics. We further evaluate the approach across three distinct embodied world-model settings, covering single-arm multi-view prediction with Ctrl-World, dual-arm single-view prediction with EVAC, and task-oriented single-arm prediction with Cosmos-Predict2.5-2B. On RoboCasa, incorporating HWM into Cosmos-Predict2.5-2B yields a 2.7% relative improvement in task success. On real-world AgiBot World trajectories, it improves nDTW by 9.7% with Ctrl-World and Logic by 8.4% with EVAC.
