论文标题
部分可观测时空混沌系统的无模型预测
GraphDistNet: A Graph-based Collision-distance Estimator for Gradient-based Trajectory Optimization
论文作者
论文摘要
轨迹优化（TO）旨在找到一系列有效状态，同时最大程度地减少成本。但是，由于计算昂贵的碰撞搜索，其良好的验证过程通常是昂贵的，否则粗略的搜索降低了系统的安全性，丢失了精确的解决方案。为了解决这些问题，我们引入了一个新的碰撞距离估计器GraphDistNet，可以通过利用基于边缘特征的卷积操作来精确编码两个几何之间的结构信息，并有效预测一批碰撞距离和梯度通过25,000个随机环境，最大20个无处不在的对象。此外，我们表明了注意机制的采用使我们的方法可以在不可预见的复杂几何形状上易于推广。我们的评估表明，GraphDistNet在模拟和现实世界任务中的最先进基线方法的表现。
Trajectory optimization (TO) aims to find a sequence of valid states while minimizing costs. However, its fine validation process is often costly due to computationally expensive collision searches, otherwise coarse searches lower the safety of the system losing a precise solution. To resolve the issues, we introduce a new collision-distance estimator, GraphDistNet, that can precisely encode the structural information between two geometries by leveraging edge feature-based convolutional operations, and also efficiently predict a batch of collision distances and gradients through 25,000 random environments with a maximum of 20 unforeseen objects. Further, we show the adoption of attention mechanism enables our method to be easily generalized in unforeseen complex geometries toward TO. Our evaluation show GraphDistNet outperforms state-of-the-art baseline methods in both simulated and real world tasks.
