  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.
