[2605.16395] OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence
[Submitted on 12 May 2026]
Title:OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence
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Abstract: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.
Comments: Project page: this https URL
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2605.16395 [cs.RO]
(or arXiv:2605.16395v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.16395
arXiv-issued DOI via DataCite
Submission history
From: Jiajian Li [view email] [v1] Tue, 12 May 2026 13:43:53 UTC (13,721 KB)
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