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About this paper
Ruiqi Ni, Ahmed H. Qureshi
A progressive learning framework, integrating a viscosity-based Eikonal equation and progressive speed scheduling, is developed for physics-informed neural motion planning. This approach achieves 2-3 times faster planning speeds than previous neural methods and over 30 times faster than classical planners, while maintaining high success rates and strong generalization capabilities across multiple complex environments without requiring expert demonstration data.
Key insights
- Regularizing the Eikonal equation with a viscosity term can transform its ill-posed nature into a smoother, unique solution, significantly improving its learnability for neural networks.
- Gradually introducing environmental complexity through progressive speed scheduling helps stabilize training of physics-informed neural networks, preventing convergence to incorrect local minimums and improving performance in obstacle-rich areas.
- A data-efficient physics-informed approach can achieve competitive or superior performance to data-driven and classical methods by refining PDE formulations and optimizing training strategies, enabling strong generalization without expert demonstrations.
Summary by alphaXiv.
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