Humanoid robots have great potential to perform various human-level skills. These skills
involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine …

Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-
manipulation, and tabletop manipulation, each demanding a different mode of control. For …

In contrast to quadruped robots that can navigate diverse terrains using a" blind" policy,
humanoid robots require accurate perception for stable locomotion due to their high degrees …

The realization of universal robots is an ultimate goal of researchers. However, a key hurdle
in achieving this goal lies in the robots' ability to manipulate objects in their unstructured …

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 …

Training robots directly from human videos is an emerging area in robotics and computer
vision. While there has been notable progress with two-fingered grippers, learning …

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 …

Humanoid robots have significant gaps in their sensing and perception, making it hard to
perform motion planning in dense environments. To address this, we introduce ARMOR, a …

Current humanoid teleoperation systems either lack reliable low-level control policies, or
struggle to acquire accurate whole-body control commands, making it difficult to teleoperate …