Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through the imitation of motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. In this paper, we focus on one class of interactive task---sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different single level and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A video highlight can be found at https://youtu.be/3CeN0OGz2cA.
翻译:物理学基础字符动画的最近进展表明,通过深层强化学习模拟运动捕获数据,人类运动合成工作取得了令人印象深刻的突破,但是,结果大多表现在模仿单一不同的运动模式上,而由于人类物体空间配置的不同,并不推广到需要灵活运动模式的交互式任务。在本文中,我们侧重于一组交互式任务 -- -- 悬浮在椅子上。我们提议了一个等级强化学习框架,它依赖于一组子任务控制器,这些子任务控制器经过训练,能够模仿简单的、可重复使用的移动动作,以及受过适当执行子任务以完成主要任务的训练的元控制器。我们实验性地展示了我们在不同单一级别和等级基线上的方法的强度。我们还表明,我们的方法可以用于在图像投入下进行运动预测。一个视频突出显示可在https://youtu./3CeNOGOG2cA上找到。