We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.

翻译：我们提出ArtiGrasp方法，用于合成包含抓取和关节运动的双手物体交互。该任务因全局手腕运动的多样性及关节运动所需的手指精细控制而具有挑战性。ArtiGrasp利用强化学习与物理模拟训练策略，以控制全局和局部手部姿态。我们的框架将抓取与关节运动统一于单一策略中，该策略由单一手部姿态参考引导。此外，为促进关节运动所需的手指精细控制训练，我们提出一种难度递增的学习课程：从对静态物体的单手操作开始，逐步演进至包含双手与非静态物体的多智能体训练。为评估方法，我们提出动态物体抓取与关节运动任务，要求将物体调整至目标关节姿态。该任务需同时实现抓取、重定位与关节运动。实验表明本方法在该任务中的有效性。进一步证明，该方法能从基于图像的现成回归器生成的含噪手-物体姿态估计中生成运动。