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.

翻译：基于物理的角色动画已成为合成逼真、物理可信运动的基本方法。尽管当前数据驱动的深度强化学习方法能够合成复杂技能，但在再现动画所需却违反物理定律的夸张风格化运动（如瞬时冲刺或空中变轨）时存在困难。其主要限制源于将角色建模为欠驱动浮基系统，其运动严格受控于内部关节力矩与动量守恒。直接通过外力旋量强制施加此类运动常导致训练不稳定，速度间断产生的稀疏高幅值力脉冲会阻碍策略收敛。我们提出助力脉冲神经控制框架，将外力辅助从力空间重构为冲量空间以保证数值稳定性。我们将助力信号分解为由逆动力学解析生成的高频分量，以及由混合神经策略调控的学习型低频残差修正项。实验表明，该方法能稳健追踪以往基于物理方法难以处理的高敏捷性、动力学不可行机动动作。