DiffuSolve: 非凸軌道最適化のための拡散モデルベースソルバー
DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization
拡散モデルを用いて非凸軌道最適化問題の初期解を生成し、数値ソルバーを効率的にウォームスタートさせる手法を提案。制約違反を低減するDiffuSolve+も導入し、計算効率を2～11倍向上させた。
著者: Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson
分類: cs.RO, cs.LG
原文アブストラクト
Optimal trajectory design is computationally expensive for nonlinear and high-dimensional dynamical systems. The challenge arises from the non-convex nature of the optimization problem with multiple local optima, which usually requires a global search. Traditional numerical solvers struggle to find diverse solutions efficiently without appropriate initial guesses. In this paper, we introduce DiffuSolve, a general diffusion model-based solver for non-convex trajectory optimization. An expressive diffusion model is trained on pre-collected locally optimal solutions and efficiently samples initial guesses, which then warm-starts numerical solvers to fine-tune the feasibility and optimality. We also present DiffuSolve+, a novel constrained diffusion model with an additional loss in training that further reduces the problem constraint violations of diffusion samples. Experimental evaluations on three tasks verify the improved robustness, diversity, and a 2$\times$ to 11$\times$ increase in computational efficiency with our proposed method, which generalizes well to trajectory optimization problems of varying challenges.
