This paper enables real-world humanoid robots to maintain stability while performing
expressive motions like humans do. We propose ExBody2, a generalized whole-body …

Learning generic skills for humanoid robots interacting with 3D scenes by mimicking human
data is a key research challenge with significant implications for robotics and real-world …

Standing-up control is crucial for humanoid robots, with the potential for integration into
current locomotion and loco-manipulation systems, such as fall recovery. Existing …

Scalable learning of humanoid robots is crucial for their deployment in real-world
applications. While traditional approaches primarily rely on reinforcement learning or …

Locomotion is a fundamental skill for humanoid robots. However, most existing works made
locomotion a single, tedious, unextendable, and passive movement. This limits the kinematic …

Automatic fall recovery is a crucial prerequisite before humanoid robots can be reliably
deployed. Hand-designing controllers for getting up is difficult because of the varied …

Deep Reinforcement Learning (RL) has emerged as a promising method to develop
humanoid robot locomotion controllers. Despite the robust and stable locomotion …