  There is growing interest in automating agricultural tasks that require
intricate and precise interaction with specialty crops, such as trees and
vines. However, developing robotic solutions for crop manipulation remains a
difficult challenge due to complexities involved in modeling their deformable
behavior. In this study, we present a framework for learning the deformation
behavior of tree-like crops under contact interaction. Our proposed method
involves encoding the state of a spring-damper modeled tree crop as a graph.
This representation allows us to employ graph networks to learn both a forward
model for predicting resulting deformations, and a contact policy for inferring
actions to manipulate tree crops. We conduct a comprehensive set of experiments
in a simulated environment and demonstrate generalizability of our method on
previously unseen trees. Videos can be found on the project website:
https://kantor-lab.github.io/tree_gnn
