Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such objects. However, the translation of motions demonstrated by a human to a robot is a challenge for IL, due to differences in the embodiments and the robot's physical limits. These limits are especially relevant in dynamic manipulation where high velocities and accelerations are typical. To address this problem, we propose a framework that first maps a dynamic demonstration into a motion that respects the robot's constraints using a constrained Dynamic Movement Primitive. Second, the resulting object state is further optimized by quasi-static refinement motions to optimize task performance metrics. This allows both efficiently altering the object state by dynamic motions and stable small-scale refinements. We evaluate the framework in the challenging task of bag opening, designing the system BILBO: Bimanual dynamic manipulation using Imitation Learning for Bag Opening. Our results show that BILBO can successfully open a wide range of crumpled bags, using a demonstration with a single bag. See supplementary material at https://sites.google.com/view/bilbo-bag.

翻译：模仿学习是一种有前景的范式，可用于学习可变形物体的动态操作，因为它无需依赖难以构建的精确模拟此类物体的模型。然而，由于人机具身差异以及机器人物理极限的限制，将人类演示的运动迁移至机器人是模仿学习面临的一大挑战。这些限制在典型涉及高速与高加速度的动态操作中尤为突出。为解决此问题，我们提出一个框架：首先通过约束动态运动基元将动态演示映射为符合机器人约束的运动；其次，通过准静态精修运动进一步优化目标物体状态，以提升任务性能指标。该方法既能通过动态运动高效改变物体状态，又能实现稳定的精细尺度调整。我们在具有挑战性的开袋任务中评估了该框架，并设计了系统BILBO：基于模仿学习的双臂动态开袋操作。实验结果表明，BILBO能够通过单一袋子的演示成功打开多种皱缩的袋子。补充材料参见https://sites.google.com/view/bilbo-bag。