Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method's effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical, real-world applications.

翻译：形状伺服控制是一种专门将物体控制至期望目标形状的机器人任务，是实现可变形物体操作的有效方法。然而，该技术存在对目标形状规范的高度依赖问题：目标形状需要通过繁琐的领域知识工程进行人工定义，或通过手动将物体塑造成期望形状并在特定时刻捕捉目标形状。这两种方法在各类机器人应用中均缺乏实用性。本文通过开发新型神经网络DefGoalNet解决该问题，该网络能直接从少量人类示范中学习可变形物体的目标形状。我们在仿真环境和实体机器人上验证了该方法在多种机器人任务中的有效性。值得注意的是，在外科牵拉任务中，即使仅使用10次示范进行训练，该方法的中位成功率仍能达到近90%。这些成果标志着在推动形状伺服方法实现可变形物体操作实用化方面取得了重大进展。