In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects. Further adapting a successful manipulation skill to new objects with different shapes and physical properties is a similarly challenging problem. In this work, we show that natural and robust in-hand manipulation of simple objects in a dynamic simulation can be learned from a high quality motion capture example via deep reinforcement learning with careful designs of the imitation learning problem. We apply our approach on both single-handed and two-handed dexterous manipulations of diverse object shapes and motions. We then demonstrate further adaptation of the example motion to a more complex shape through curriculum learning on intermediate shapes morphed between the source and target object. While a naive curriculum of progressive morphs often falls short, we propose a simple greedy curriculum search algorithm that can successfully apply to a range of objects such as a teapot, bunny, bottle, train, and elephant.

翻译：手内物体操作因复杂的接触动力学、非重复性手指步态以及需要间接控制非驱动物体而难以模拟。将成功的操作技能进一步迁移至具有不同形状和物理特性的新物体同样具有挑战性。在本工作中，我们通过深度强化学习，精心设计模仿学习问题，基于高质量运动捕捉示例，展示了在动态仿真中学习简单物体的自然且鲁棒的手内操作。我们将该方法应用于多种物体形状和运动的单手及双手灵巧操作。随后，我们通过源物体与目标物体之间中间形状的形态变换课程学习，进一步展示了将示例运动迁移至更复杂形状的能力。虽然朴素的渐进形态课程学习往往效果不佳，但我们提出了一种简单的贪婪课程搜索算法，该算法可成功应用于茶壶、兔子、瓶子、火车和大象等一系列物体。