Abstract:
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

Committee:
Prof. George Kantor (Chair)
Prof. Oliver Kroemer
Prof. David Held
Kevin Zhang