论文标题
从专家演示中学习可变形的对象操纵
Learning Deformable Object Manipulation from Expert Demonstrations
论文作者
论文摘要
我们提供了一种从演示方法（LFD）方法的新颖学习，即演示（DMFD）的可变形操作，以使用状态或图像作为输入来解决可变形的操纵任务，给定专家演示。我们的方法以三种不同的方式使用演示，并平衡在线探索环境与专家指导之间有效地探索高维空间之间的权衡。我们在一组一维绳索的一组代表性操纵任务上测试DMFD，并从软件套件中的一组绳索和2维布进行测试，每个布都带有状态和图像观测。对于基于州的任务，我们的方法超过了基线性能高达12.9％，在基于图像的任务上最多可超过33.44％，具有可比或更好的随机性。此外，我们创建了两个具有挑战性的环境，用于使用基于图像的观测值折叠2D布，并为其设定性能基准。与仿真相比，我们在现实世界执行过程中归一化性能的损失最小的真实机器人部署了DMFD（约6％）。源代码在github.com/uscresl/dmfd上
We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the environment online and using guidance from experts to explore high dimensional spaces effectively. We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations. Our method exceeds baseline performance by up to 12.9% for state-based tasks and up to 33.44% on image-based tasks, with comparable or better robustness to randomness. Additionally, we create two challenging environments for folding a 2D cloth using image-based observations, and set a performance benchmark for them. We deploy DMfD on a real robot with a minimal loss in normalized performance during real-world execution compared to simulation (~6%). Source code is on github.com/uscresl/dmfd
