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Constraint-Aware Diffusion Models for Trajectory Optimization
The diffusion model has shown success in generating high-quality and diverse solutions to trajectory optimization problems.
Combining Constrained Diffusion Models and Numerical Solvers for Efficient and Robust Non-Convex Trajectory Optimization
Motivated by the need to solve open-loop optimal control problems with computational efficiency and reliable constraint satisfaction, we introduce a general framework that combines diffusion models and numerical optimization solvers.
Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models
Preliminary trajectory design is a global search problem that seeks multiple qualitatively different solutions to a trajectory optimization problem.
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed.
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