Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects. However, achieving these gaits with arbitrary orientations of the hand is challenging due to the unstable nature of the task. In this work, we use model-free reinforcement learning (RL) to learn finger-gaiting only via precision grasps and demonstrate finger-gaiting for rotation about an axis purely using on-board proprioceptive and tactile feedback. To tackle the inherent instability of precision grasping, we propose the use of initial state distributions that enable effective exploration of the state space. Our method can learn finger-gaiting with significantly improved sample complexity than the state-of-the-art. The policies we obtain are robust and also transfer to novel objects.

翻译：手指操纵是实现大角在手上调整物体方向的重要技能。 但是,由于任务不稳定性,以专横的手法完成这些曲子具有挑战性。 在这项工作中,我们使用无型强化学习(RL)只通过精确的掌握来学习手指取法,并展示纯粹使用机载自行感知和触觉反馈来旋转轴的手指取法。为了解决精确捕捉的内在不稳定性,我们提议使用初始状态分布,以便能够有效地探索国家空间。我们的方法可以学习手指取法,其样本复杂性比最新工艺要高得多。我们获得的政策是稳健的,也转移到了新东西。