This paper presents a physics-based humanoid control framework, aiming to mastering highly-dynamic human behaviors such as Kungfu and dancing through physics-based motion selection and adaptive motion imitation.

We propose an adversarial policy learning framework for humanoid robots, aiming for precise motion tracking of the upper body and robust locomotion of the lower body.

This paper presents HumanoidGen, an automated task creation and demonstration collection framework that leverages atomic dexterous skills and LLM reasoning to generate relational constraints.