将世界模型变为可微分物理引擎，提升机器人模拟与控制效果。
OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence
- 用可微分框架统一场景、神经动态与强化学习
- 支持接触力建模与稀疏奖励下的梯度优化
- 适合需要物理精度的机器人仿真与策略训练
我们提出OrbiSim，一种新型机器人仿真范式，将世界模型重构为面向具身智能的全可微分物理引擎。不同于以往聚焦于潜在或视觉空间中自由想象的世界模型，OrbiSim建立了一条融合结构化场景资产、神经动力学与下游强化学习的统一物理基础路径。通过在整个仿真循环中实现端到端可微分性——涵盖显式状态转移至视觉观测生成——OrbiSim支持传统仿真器难以处理的任务，如可微分接触建模、稀疏奖励下的梯度策略优化以及直观物理推理。实验证明，OrbiSim在预测保真度和控制性能上均显著优于现有先进世界模型。其对资产配置和物理参数的一致响应，表明其在增强机器人仿真与策略训练方面具有潜力。
原文摘要 · Abstract (English)
We present OrbiSim, a novel robotic simulation paradigm that redefines world models as a fully differentiable physics engine for embodied intelligence. Unlike prior world models that focus on unconstrained imagination in latent or visual domains, OrbiSim establishes a unified, physically-grounded pathway that bridges structured scene assets, neural dynamics, and downstream reinforcement learning. By enabling end-to-end differentiability throughout the entire simulation loop -- spanning from explicit state transitions to visual observation generation -- OrbiSim supports tasks traditionally intractable for classical simulators, such as differentiable contact modeling, gradient-based policy optimization under sparse rewards, and intuitive physical inference. Empirical results demonstrate that OrbiSim significantly outperforms state-of-the-art world models in both predictive fidelity and control performance. Furthermore, its consistent responsiveness to asset configurations and physical parameters suggests its potential as a differentiable tool for enhancing robot simulation and policy training.
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