MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy
arXiv:2605.22597v1 Announce Type: new Abstract: Learning real-world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physical parameters, yet accuracy is ultimately bounded by the underlying physical models, which often assume materials are homogeneous and isotropic. Even if reasonable, real-world objects typically exhibit mild anisotropy and heterogeneity. After the near-isotropic backbone is well calibrated, these residual effects become the key bottleneck for further closing the real-to-sim gap. Although neural netw
The accelerating pace of AI development requires more accurate real-world interaction for autonomous systems, pushing research into precise simulation-to-reality transfer.
Improving the accuracy of real-to-sim transfer is crucial for the safe and effective deployment of AI and robotics in complex physical environments, reducing development costs and risks.
This advancement allows for AI models to learn dynamics that are closer to real-world complexities, like material anisotropies, improving performance beyond idealized simulations.
- · Robotics developers
- · AI simulation companies
- · Autonomous systems manufacturers
- · Manufacturing sector
- · Companies reliant solely on simplified physics engines
- · Manual calibration processes
Reduced real-world training requirements for robotic systems due to enhanced simulation fidelity.
Faster iteration and deployment cycles for AI-driven physical applications, accelerating innovation.
Increased reliability and safety of autonomous systems operating in unstructured, complex environments.
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