ポテンシャルベース拡散運動計画
Potential Based Diffusion Motion Planning
ポテンシャルベースの運動計画をニューラルネットワークで学習し、拡散モデルを用いて局所解を回避しながら高次元空間での計画を可能にした手法を提案。
著者: Yunhao Luo, Chen Sun, Joshua B. Tenenbaum, Yilun Du
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Effective motion planning in high dimensional spaces is a long-standing open problem in robotics. One class of traditional motion planning algorithms corresponds to potential-based motion planning. An advantage of potential based motion planning is composability -- different motion constraints can be easily combined by adding corresponding potentials. However, constructing motion paths from potentials requires solving a global optimization across configuration space potential landscape, which is often prone to local minima. We propose a new approach towards learning potential based motion planning, where we train a neural network to capture and learn an easily optimizable potentials over motion planning trajectories. We illustrate the effectiveness of such approach, significantly outperforming both classical and recent learned motion planning approaches and avoiding issues with local minima. We further illustrate its inherent composability, enabling us to generalize to a multitude of different motion constraints.
