Humanoid Motion

Humanoid motion research aims to enable robots to perform complex, human-like movements, focusing on dexterity, balance, and whole-body control. Current efforts leverage reinforcement learning, often coupled with model predictive control or transformer-based architectures like GPT, to learn control policies from large motion datasets or through human teleoperation. This research is significant for advancing robotics capabilities in areas such as manipulation, locomotion, and human-robot interaction, with implications for manufacturing, healthcare, and other fields. The development of universal motion representations and modular control architectures promises more efficient and robust humanoid control.