Humanoid Control

Humanoid control research aims to develop algorithms enabling robots to perform complex, human-like movements. Current efforts focus on learning-based approaches, including reinforcement learning and imitation learning, often utilizing transformer architectures and leveraging large motion capture datasets to train controllers capable of diverse tasks like walking, manipulation, and even parkour. These advancements are significant for robotics, enabling more versatile and robust robots, and also impact fields like animation and virtual character creation through the development of more realistic and physically plausible models.