Virtual character animation and movement synthesis have advanced rapidly during recent years, especially through a combination of extensive motion capture datasets and machine learning. A remaining challenge is interactively simulating characters that fatigue when performing extended motions, which is indispensable for the realism of generated animations. However, capturing such movements is problematic, as performing movements like backflips with fatigued variations up to exhaustion raises capture cost and risk of injury. Surprisingly, little research has been done on faithful fatigue modeling. To address this, we propose a deep reinforcement learning-based approach, which -- for the first time in literature -- generates control policies for full-body physically simulated agents aware of cumulative fatigue. For this, we first leverage Generative Adversarial Imitation Learning (GAIL) to learn an expert policy for the skill; Second, we learn a fatigue policy by limiting the generated constant torque bounds based on endurance time to non-linear, state- and time-dependent limits in the joint-actuation space using a Three-Compartment Controller (3CC) model. Our results demonstrate that agents can adapt to different fatigue and rest rates interactively, and discover realistic recovery strategies without the need for any captured data of fatigued movement.

翻译：虚拟角色动画与运动合成在近年来取得了快速发展，尤其是通过大规模运动捕捉数据集与机器学习的结合。当前仍存的挑战在于交互式模拟角色在执行持续性动作时产生的疲劳效果——这对生成动画的真实感至关重要。然而，捕捉这类动作存在困难，例如完成带疲劳变量的后空翻直至力竭的动作，会显著增加捕捉成本与受伤风险。令人惊讶的是，针对疲劳建模的忠实性研究仍十分有限。为解决这一问题，我们提出了一种基于深度强化学习的方法——这在文献中首次生成了面向全身物理仿真代理的、具有累积疲劳感知能力的控制策略。具体而言，我们首先利用生成对抗模仿学习（GAIL）学习动作的专家策略；其次，通过基于耐力时间限制恒定转矩范围，在关节驱动空间中采用非线性、状态与时间相关的三室控制器（3CC）模型学习疲劳策略。实验结果表明，代理能够交互式地适应不同的疲劳与休息速率，并在无需任何疲劳动作捕捉数据的情况下自发发现真实的恢复策略。