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

翻译：随着农业自动化对特殊作物（如树木和藤蔓）进行精细且精准交互的需求日益增长，开发用于作物操控的机器人解决方案仍面临重大挑战，这主要源于其可变形行为建模的复杂性。本研究提出了一种框架，用于学习树状作物在接触交互下的形变行为。该方法将采用弹簧-阻尼模型建模的树状作物状态编码为图结构。通过这种表示，我们可利用图网络同时学习预测形变结果的前向模型，以及推断操控动作的接触策略。我们在仿真环境中开展了全面的实验，并在未见过的树木上验证了该方法的泛化能力。相关视频可查阅项目网站：https://kantor-lab.github.io/tree_gnn