Sobolev-trained diffusion policies using trajectories and feedback gains provide warm-starts that reduce trajectory optimization solving time by 2x to 20x while avoiding compounding errors.
In: IEEE Inter- national Symposium on System Integrations (SII) (2019)
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Accelerating trajectory optimization with Sobolev-trained diffusion policies
Sobolev-trained diffusion policies using trajectories and feedback gains provide warm-starts that reduce trajectory optimization solving time by 2x to 20x while avoiding compounding errors.