运动牙牙学语:形态驱动的铰接字符协调控制

Avinash Ranganath, Avishek Biswas, Ioannis Karamouzas, V. Zordan
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引用次数: 1

摘要

人类和动物的运动是高度协调的,许多关节一起运动。在没有参考运动数据的情况下,在铰接虚拟角色中学习类似的协调运动是一项具有挑战性的任务,因为自由度和冗余度很高。在本文中,我们提出了一种在低维潜在空间中学习虚拟角色运动的方法,该方法定义了不同关节如何一起运动。我们介绍了一种称为运动胡话的技术,其中一个角色通过不协调的低水平(运动)激励来驱动其关节,从而与环境相互作用,从而产生一个运动数据的语料,从中提取出一个流形潜在空间。提取的流形的维度定义了与字符相关的各种各样的协同作用,通过强化学习,我们通过选择一小组适当的潜在维度来训练字符在潜在空间中学习运动,同时学习相应的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motor Babble: Morphology-Driven Coordinated Control of Articulated Characters
Locomotion in humans and animals is highly coordinated, with many joints moving together. Learning similar coordinated locomotion in articulated virtual characters, in the absence of reference motion data, is a challenging task due to the high number of degrees of freedom and the redundancy that comes with it. In this paper, we present a method for learning locomotion for virtual characters in a low dimensional latent space which defines how different joints move together. We introduce a technique called motor babble, wherein a character interacts with its environment by actuating its joints through uncoordinated, low-level (motor) excitations, resulting in a corpus of motion data from which a manifold latent space is extracted. Dimensions of the extracted manifold define a wide variety of synergies pertaining to the character and, through reinforcement learning, we train the character to learn locomotion in the latent space by selecting a small set of appropriate latent dimensions, along with learning the corresponding policy.
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