ArtiGrasp：双手灵巧抓取与铰接运动的物理合理合成
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
AI总结：
本文提出 ArtiGrasp，通过强化学习、物理仿真和难度递进课程训练统一策略，合成物理合理的双手抓取与铰接运动，并验证其在动态任务及带噪视觉姿态估计下的有效性。
摘要：
我们提出 ArtiGrasp，这是一种合成包含抓取和铰接运动的双手手—物交互的新方法。由于驱动物体铰接所需的全局腕部运动具有多样性，并且需要精确的手指控制，该任务颇具挑战性。ArtiGrasp 利用强化学习和物理仿真来训练一个控制手部全局与局部姿态的策略。我们的框架在单一策略中统一了抓取与铰接，并由单一手部姿态参考进行引导。此外，为促进铰接所需的精确手指控制训练，我们提出了一种难度递增的学习课程。该课程从对静止物体的单手操作开始，随后进入包含双手和非静止物体的多智能体训练。为评估我们的方法，我们引入 Dynamic Object Grasping and Articulation（动态物体抓取与铰接）任务，其目标是将物体带到目标铰接姿态。该任务需要抓取、重新定位和铰接。我们展示了本方法在这一任务上的有效性，并进一步证明，本方法能够利用来自现成的基于图像的回归器所产生的带噪声手—物姿态估计来生成运动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
