A novel diffusion variant accelerates minimum-time planning for redundant dual-arm robots by replacing gradient-based solving of the nonconvex high-level problem with probabilistic sampling, yielding 35x faster runtime and 34% less path error.
An interior algorithm for nonlinear optimization that combines line search and trust region steps
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Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
A novel diffusion variant accelerates minimum-time planning for redundant dual-arm robots by replacing gradient-based solving of the nonconvex high-level problem with probabilistic sampling, yielding 35x faster runtime and 34% less path error.