Teleoperation serves as a powerful method for collecting on-robot data essential for robot
learning from demonstrations. The intuitiveness and ease of use of the teleoperation system …

We study the problem of mobile manipulation using legged robots equipped with an arm,
namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an …

Can we enable humanoid robots to generate rich, diverse, and expressive motions in the
real world? We propose to learn a whole-body control policy on a human-sized robot to …

Robust locomotion control depends on accurate state estimations. However, the sensors of
most legged robots can only provide partial and noisy observations, making the estimation …

Parkour poses a significant challenge for legged robots, requiring navigation through
complex environments with agility and precision based on limited sensory inputs. In this …

Learning-based algorithms have demonstrated impressive performance in agile locomotion
of legged robots. However, learned policies are often complex and opaque due to the black …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

This work introduces DiffuseLoco, a framework for training multi-skill diffusion-based policies
for dynamic legged locomotion from offline datasets, enabling real-time control of diverse …

Humanoid robots require both robust lower-body locomotion and precise upper-body
manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body …

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