How DiffTaichi's differentiable physics simulation uses gradients to drive control optimization

The article analyzes the adjoint gradient mechanism of DiffTaichi's differentiable physics simulation, using loss gradients to directly optimize actuator control sequences in material point method soft body, mass-spring, and fluid scenarios. It shows that gradient methods can optimize soft-body motion forms that reinforcement-learning-style sampling methods struggle to reach.