Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard perception.
Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation.
Curriculum learning has demonstrated substantial effectiveness in robot learning.
Effective motion representation is crucial for enabling robots to imitate expressive behaviors in real time, yet existing motion controllers often ignore inherent patterns in motion.