  Humanoid Reaction Synthesis is pivotal for creating highly interactive and
empathetic robots that can seamlessly integrate into human environments,
enhancing the way we live, work, and communicate. However, it is difficult to
learn the diverse interaction patterns of multiple humans and generate
physically plausible reactions. The kinematics-based approaches face
challenges, including issues like floating feet, sliding, penetration, and
other problems that defy physical plausibility. The existing physics-based
method often relies on kinematics-based methods to generate reference states,
which struggle with the challenges posed by kinematic noise during action
execution. Constrained by their reliance on diffusion models, these methods are
unable to achieve real-time inference. In this work, we propose a Forward
Dynamics Guided 4D Imitation method to generate physically plausible human-like
reactions. The learned policy is capable of generating physically plausible and
human-like reactions in real-time, significantly improving the speed(x33) and
quality of reactions compared with the existing method. Our experiments on the
InterHuman and Chi3D datasets, along with ablation studies, demonstrate the
effectiveness of our approach.
