这篇论文把物理规律直接塞进潜空间，导航成功率涨了 8 个点，碰撞率砍半，做具身智能的值得看看。
arXiv 新论文提出能量结构潜在世界模型（ELWM），将能量和动量显式编码进潜状态，通过耗散和控制端口保证严格因果转换。基于 RGB-D 和惯性交互历史训练，ELWM 确保预测物理一致。配套的物理条件神经时间场（PC-NTF）通过 Eikonal 方程集成到到达时间场，生成导航策略。在跨场景评估中，PC-NTF 将 0.8 秒运动预测 NRMSE 从 0.36 降至 0.29，导航成功率从 81.3% 提升至 89.7%，SPL 从 0.64 升至 0.73，物理碰撞率从 12.1% 降至 5.8%。
Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning
Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics. While latent world models offer a promising approach by predicting these dynamics, existing methods learn unconstrained future representations where absorbed physics remains implicit. Therefore, they fail to form reusable physical knowledge, which compromises reliability in unpredictable open-world navigation. To address this, we propose a novel Energy-Structured Latent World Model (ELWM). Our key idea is to structure the ELWM latent state to explicitly carry energy and momentum, ensuring strictly causal transitions via dissipation and control ports. Trained on multimodal RGB-D and inertial interaction histories, our model guarantees physically consistent predictions. We further implement this for motion planning by constructing Physics-Conditioned Neural Time Fields (PC-NTF), a key technical cornerstone that integrates ELWM into an arrival time field via the Eikonal equation to yield a physically-informed navigation policy. Across held-out scenes, our evaluation reveals significant improvements. Compared to generic latent models, PC-NTF reduces 0.8-s motion-prediction NRMSE from 0.36 to 0.29. Against Active Neural Time Fields, it improves navigation success from 81.3% to 89.7% and SPL from 0.64 to 0.73, while cutting the physical collision rate from 12.1% to 5.8% and the Eikonal residual from 0.083 to 0.031. Beyond these targeted gains, our results demonstrate that embedding explicit physical structures into latent spaces intrinsically bridges the gap between predictive world models and safe, dynamically feasible motion planning.