让物理角色动画能精准实现夸张动作，如瞬间冲刺和空中变向。
Neural Assistive Impulses: Synthesizing Exaggerated Motions for Physics-based Characters
- 用冲量空间替代力空间控制，提升训练稳定性。
- 分解辅助信号为解析高频部分和学习低频修正，实现精准轨迹跟踪。
- 适合需要超现实动作的动画制作，如游戏与影视特效。
基于物理的角色动画已成为生成真实、物理合理动作的核心方法。尽管当前数据驱动的深度强化学习（DRL）方法可合成复杂技能，却难以再现夸张、风格化的动作，如瞬间冲刺或空中轨迹突变，这类动作虽在动画中常见，但违反标准物理规律。主要瓶颈在于将角色建模为欠驱动浮地系统，内部关节扭矩与动量守恒严格限制运动。直接通过外力施加此类动作常导致训练不稳，因速度突变产生稀疏且高幅值的力脉冲，阻碍策略收敛。本文提出神经辅助冲量控制（Neural Assistive Impulses），将外部协助重新表述在冲量空间而非力空间，以确保数值稳定。我们将辅助信号分解为由逆动力学导出的解析高频成分与由混合神经策略控制的学习低频残差修正。实验证明，该方法可稳健追踪此前基于物理的方法无法实现的高敏捷、动态不可行的机动动作。
原文摘要 · Abstract (English)
Physics-based character animation has become a fundamental approach for synthesizing realistic, physically plausible motions. While current data-driven deep reinforcement learning (DRL) methods can synthesize complex skills, they struggle to reproduce exaggerated, stylized motions, such as instantaneous dashes or mid-air trajectory changes, which are required in animation but violate standard physical laws. The primary limitation stems from modeling the character as an underactuated floating-base system, in which internal joint torques and momentum conservation strictly govern motion. Direct attempts to enforce such motions via external wrenches often lead to training instability, as velocity discontinuities produce sparse, high-magnitude force spikes that prevent policy convergence. We propose Assistive Impulse Neural Control, a framework that reformulates external assistance in impulse space rather than force space to ensure numerical stability. We decompose the assistive signal into an analytic high-frequency component derived from Inverse Dynamics and a learned low-frequency residual correction, governed by a hybrid neural policy. We demonstrate that our method enables robust tracking of highly agile, dynamically infeasible maneuvers that were previously intractable for physics-based methods.
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